Academic literature on the topic 'Surrogate Function'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Surrogate Function.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Journal articles on the topic "Surrogate Function"
1
THIEL, MARCO, M. CARMEN ROMANO, UDO SCHWARZ, JÜRGEN KURTHS, and JENS TIMMER. "SURROGATE-BASED HYPOTHESIS TEST WITHOUT SURROGATES." International Journal of Bifurcation and Chaos 14, no. 06 (June 2004): 2107–14. http://dx.doi.org/10.1142/s0218127404010527.
Full textAbstract:
Fourier surrogate data are artificially generated time series, that — based on a resampling scheme — share the linear properties with an observed time series. In this paper we study a statistical surrogate hypothesis test to detect deviations from a linear Gaussian process with respect to asymmetry in time (Q-statistic). We apply this test to a Fourier representable function and obtain a representation of the asymmetry in time of the sample data, a characteristic for nonlinear processes, and the significance in terms of the Fourier coefficients. The main outcome is that we calculate the expected value of the mean and the standard deviation of the asymmetries of the surrogate data analytically and hence, no surrogates have to be generated. To illustrate the results we apply our method to the saw tooth function, the Lorenz system and to measured X-ray data of Cygnus X-1.
2
Ziff, Elizabeth. "“Honey, I Want to Be a Surrogate”: How Military Spouses Negotiate and Navigate Surrogacy With Their Service Member Husbands." Journal of Family Issues 40, no. 18 (July 18, 2019): 2774–800. http://dx.doi.org/10.1177/0192513x19862843.
Full textAbstract:
This article examines how military spouses negotiate the decision to become a surrogate with their service member husband and how the two navigate surrogacy together. It is speculated that military spouses are ideal candidates for surrogacy due to their particular status as a military spouse; however, military spouses face structural constraints in their everyday lives which in turn would prove challenging to their desire to become a surrogate. Based on in-depth interviews with 33 military spouses who had been surrogates, this article examines how military spouses discuss, negotiate, and experience surrogacy with their spouses all the while navigating the structural demands of the military and the contractual demands of surrogacy. Findings highlight egalitarian decision making between the spouses, and a mostly collaborative approach to the surrogacy process. Ultimately, this work illuminates how surrogacy is experienced by the women who participate in the practice and provides insight as to how military marriages function.
3
Ambarwati, Mega Dewi, and Ghina Azmita Kamila. "THE EVALUATION OF SURROGACY’S LEGAL SYSTEM IN INDONESIA AS COMPARISON TO INDIA’S LEGISLATION." Diponegoro Law Review 4, no. 2 (October 1, 2019): 167. http://dx.doi.org/10.14710/dilrev.4.2.2019.167-180.
Full textAbstract:
Nowadays, in marriage life, spouse often dealing with big problem as like infertility which make them unable to have offspring. However, due to infertility, the spouse has obtained some efforts to solve their problems. One way to solve the problem is by obtaining surrogacy with the help of surrogate mother. Nevertheless, in Indonesia, especially, surrogacy as well as surrogate mother is still considered to be taboo things and no legal system which regulate the surrogacy and/or surrogate mother. Yet other countries have allowed or legalize the surrogacy practice as well as surrogate mother. Hence, this study aimed to reveal a comparison of legal system on surrogate mother and surrogacy law in Indonesia and India. This study used comparative legal research methodology through the functional method since Indonesia has the same function over the purpose of law establishment on the surrogate mother in India. The result reveals that it needs a legal system on surrogacy and surrogate mother as the legal certainty for any individual especially spouse who could not have offspring along with some reasons such as minimalize prostitution and unregistered marriage, prevent dispute, and to develop scientific field.
4
Tenne, Yoel. "An Analysis of the RBF Hyperparameter Impact on Surrogate-Assisted Evolutionary Optimization." Scientific Programming 2022 (December 20, 2022): 1–12. http://dx.doi.org/10.1155/2022/5175941.
Full textAbstract:
Computationally expensive optimization problems are often solved using surrogates and a common variant is the radial basis functions (RBF) model. It aggregates several basis functions which all depend on a hyperparameter affecting their individual outputs and consequentially the overall surrogate prediction. However, the optimal value of the hyperparameter is typically unknown and should therefore be calibrated. This raises the question how does the hyperparameter affect the overall optimization search effectiveness (and not just the stand-alone surrogate accuracy) and to what extent is such a calibration beneficial, which is an important consideration both for end-users and algorithm researchers alike. To rigorously address this issue this paper presents an analysis based on an extensive set of numerical experiments with an RBF surrogate-assisted evolutionary algorithm. It follows that the hyperparameter strongly affected performance and that the extent of its impact varied depending on the basis function, objective function modality, and problem dimension. Overall, calibration of the hyperparameter was typically highly beneficial to the search performance while dynamically optimizing the hyperparameter during the search yielded additional performance gains.
5
Liu, Bolin, and Liyang Xie. "Reliability Analysis of Structures by Iterative Improved Ensemble of Surrogate Method." Shock and Vibration 2019 (October 24, 2019): 1–13. http://dx.doi.org/10.1155/2019/6357104.
Full textAbstract:
Surrogate models have been widely adopted for reliability analysis. The common approach is to construct a series of surrogates based on a training set and then pick out the best one with the highest accuracy as an approximation of the time-consuming limit state function. However, the traditional method increases the risk of adopting an inappropriate model and does not take full advantage of the data devoted to constructing different surrogates. Furthermore, obtaining more samples is very expensive and sometimes even impossible. Therefore, to save the cost of constructing the surrogate and improve the prediction accuracy, an ensemble strategy is proposed in this paper for efficiently analyzing the structural reliability. The values of the weights are obtained by a recursive process and the leave-one-out technique, in which the values are updated in each iteration until a given prediction accuracy is achieved. Besides, a learning function is used to guide the selection of the next sampling candidate. Because the learning function utilizes the uncertainty estimator of the surrogate to guide the design of experiments (DoE), to accurately calculate the uncertainty estimator of the ensemble of surrogates, the concept of weighted mean square error is proposed. After the high-quality ensemble of surrogates of the limit state function is available, the Monte Carlo method is employed to calculate the failure probabilities. The proposed method is evaluated by three analytic problems and one engineering problem. The results show that the proposed ensemble of surrogates has better prediction accuracy and robustness than the stand-alone surrogates and the existing ensemble techniques.
6
Zeng, Wei, Xian Chao Wang, and Ying Sheng Wang. "Surrogating for High Dimensional Computationally Expensive Multi-Modal Functions with Elliptical Basis Function Models." Applied Mechanics and Materials 733 (February 2015): 880–84. http://dx.doi.org/10.4028/www.scientific.net/amm.733.880.
Full textAbstract:
In the engineering design process, approximation Technique could guarantee the fitting precision, speed up the design process and reduce design costs. To a certain extent, surrogate models could replace time-consuming and highly accurate computational fluid dynamics analysis gradually. In this paper, we take Optimal Latin Hypercube Sampling experimental design strategies to determine the sample space and error analysis test sample, adopt the principle of infilling criteria based on the maximum error to improve the accuracy of the surrogate model, test the unimodal and multimodal expensive functions of 10 dimension, 20 dimensions and 30 dimensions, study the performance and scope of EBF-NN surrogate model based on infilling criteria by comparing the RBF-NN surrogate model.
7
Iuliano, Emiliano. "Efficient Design Optimization Assisted by Sequential Surrogate Models." International Journal of Aerospace Engineering 2019 (May 12, 2019): 1–34. http://dx.doi.org/10.1155/2019/4937261.
Full textAbstract:
The paper proposes a global optimization algorithm employing surrogate modeling and adaptive infill criteria. The surrogates are exploited to screen the design space and provide lower-fidelity predictions across it; on the other hand, specific criteria are designed to suggest new points for high-fidelity evaluation so as to enrich the optimizer database. Both Kriging and radial basis function network are used as surrogates with different training strategies. Sequential design is achieved by introducing several infill criteria according to the realization of the exploration-exploitation trade-off. Optimization results are provided both for scalable and analytical test functions and for a practical aerodynamic shape optimization problem.
8
Malmquist, Anna, and Sonja Höjerström. "Constructions of surrogates, egg donors, and mothers: Swedish gay fathers’ narratives." Feminism & Psychology 30, no. 4 (May 14, 2020): 508–28. http://dx.doi.org/10.1177/0959353520922415.
Full textAbstract:
The study explored in detail how Swedish gay fathers (through surrogacy) talked about the surrogate mother and the egg donor. Thirteen semi-structured interviews with 22 gay fathers were conducted and analysed using critical discursive analysis. The surrogates were primarily constructed as a close family member, but occasionally in terms of their instrumental function. They were often described as active and independent, but occasionally as vulnerable or exploited. The egg donors were in some interviews constructed as close family members, while others talked about them as distant acquaintances. Further, donors were constructed either as a significant individual (for the fathers), or as an instrumental provider of the oocyte. While some participants constructed the surrogate and/or donor as their child’s mother(s), others were more reluctant or ambivalent about the mother construct. In conclusion, the participants engaged in rhetorical work that shed a positive light on surrogacy, and their own decisions were depicted as solid, ethical and genuine. The participants’ positive framing can be understood as the production of a counter discourse, in relation to an ongoing debate in Sweden, in which surrogacy is constructed as exploitation, dehumanization and prostitution.
9
&NA;. "Is endothelial function a useful surrogate?" Inpharma Weekly &NA;, no. 1256 (September 2000): 2. http://dx.doi.org/10.2165/00128413-200012560-00002.
Full text
10
Chodos, Alan, and Eric Myers. "Testing the surrogate zeta-function method." Canadian Journal of Physics 64, no. 5 (May 1, 1986): 633–36. http://dx.doi.org/10.1139/p86-117.
Full textAbstract:
Use of the surrogate zeta-function method was crucial in calculating the Casimir energy in non-Abelian Kaluza–Klein theories. We establish the validity of this method for the case where the background metric is (Euclidean space) × (N sphere). Our techniques do not apply to the case where the background is (Minkowski space) × (N sphere).
Dissertations / Theses on the topic "Surrogate Function"
1
Eisner, Mariah Claire. "Comparing Logit and Hinge Surrogate Loss Functions in Outcome Weighted Learning." The Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1585657996755039.
Full text
2
Smith, Nicola Marianne Godwin. "Characterisation of T cell surface phenotype and effector function in a surrogate model of rheumatoid arthritis." Thesis, Imperial College London, 2009. http://hdl.handle.net/10044/1/4391.
Full textAbstract:
TNFα plays a pivotal role in the pathogenesis of rheumatoid arthritis (RA), however the mechanisms underlying its dysregulation are not completely understood. TNFα production by macrophages is dependent on their contact with synovial T cells. In an in vitro model of RA, peripheral blood lymphocytes stimulated with a cocktail of cytokines mimic this RA T cell effector function. This thesis defines and characterises the effector population of cytokine-activated human T cells through two different approaches. Studies presented here show that within a population of cytokine-activated T cells, CD4+CD45RO+CCR7-cells induce the highest levels of TNFα when co-cultured with monocytes. Cytokine-activated CD4+ memory T cells phenotypically and functionally resemble lymphocytes isolated from RA synovial tissue. The cytokine cocktail induces proliferation and differentiation of peripheral blood T cells into highly potent effectors. These cells upregulate specific activation markers, adhesion molecules and chemokine receptors; such as CD25, CD69, CD62L, VLA-4, LFA-1 and CXCR4 which directly or indirectly, contribute to the induction of TNFα. By defining the phenotype of the lymphocytes most capable of inducing TNFα in our model, I isolated a population of T cells on which to focus my studies. The molecular nature of contact-dependent monocyte activation by cytokine-activated T cells was further investigated through proteomic profiling of the T cell surface. Plasma membrane protein-enriched samples were resolved in one- and two-dimensions. Subsequent mass spectrometry identified two molecules of interest. CD97 was found to be highly expressed by cytokine-activated CD4+ memory T cells, and contributed to both the induction of monocyte TNFα and spontaneous TNFα release from rheumatoid synovial tissue. Expression of CD81 and other tetraspanin family members increased on cytokine activation and was observed in synovial tissue. The results presented in this thesis provide further insight into the contribution of T cells in RA.
3
Sarmiento, Alam Natalia Catalina [Verfasser], Johannes [Akademischer Betreuer] [Gutachter] Buchner, and Bernd [Gutachter] Reif. "Structure and function of the surrogate light chain / Natalia Catalina Sarmiento Alam ; Gutachter: Bernd Reif, Johannes Buchner ; Betreuer: Johannes Buchner." München : Universitätsbibliothek der TU München, 2015. http://d-nb.info/1133261825/34.
Full text
4
Yu, Jiaqian. "Minimisation du risque empirique avec des fonctions de perte nonmodulaires." Thesis, Université Paris-Saclay (ComUE), 2017. http://www.theses.fr/2017SACLC012/document.
Full textAbstract:
Cette thèse aborde le problème de l’apprentissage avec des fonctions de perte nonmodulaires. Pour les problèmes de prédiction, où plusieurs sorties sont prédites simultanément, l’affichage du résultat comme un ensemble commun de prédiction est essentiel afin de mieux incorporer les circonstances du monde réel. Dans la minimisation du risque empirique, nous visons à réduire au minimum une somme empirique sur les pertes encourues sur l’échantillon fini avec une certaine perte fonction qui pénalise sur la prévision compte tenu de la réalité du terrain. Dans cette thèse, nous proposons des méthodes analytiques et algorithmiquement efficaces pour traiter les fonctions de perte non-modulaires. L’exactitude et l’évolutivité sont validées par des résultats empiriques. D’abord, nous avons introduit une méthode pour les fonctions de perte supermodulaires, qui est basé sur la méthode d’orientation alternée des multiplicateurs, qui ne dépend que de deux problémes individuels pour la fonction de perte et pour l’infèrence. Deuxièmement, nous proposons une nouvelle fonction de substitution pour les fonctions de perte submodulaires, la Lovász hinge, qui conduit à une compléxité en O(p log p) avec O(p) oracle pour la fonction de perte pour calculer un gradient ou méthode de coupe. Enfin, nous introduisons un opérateur de fonction de substitution convexe pour des fonctions de perte nonmodulaire, qui fournit pour la première fois une solution facile pour les pertes qui ne sont ni supermodular ni submodular. Cet opérateur est basé sur une décomposition canonique submodulairesupermodulaireThis thesis addresses the problem of learning with non-modular losses. In a prediction problem where multiple outputs are predicted simultaneously, viewing the outcome as a joint set prediction is essential so as to better incorporate real-world circumstances. In empirical risk minimization, we aim at minimizing an empirical sum over losses incurred on the finite sample with some loss function that penalizes on the prediction given the ground truth. In this thesis, we propose tractable and efficient methods for dealing with non-modular loss functions with correctness and scalability validated by empirical results. First, we present the hardness of incorporating supermodular loss functions into the inference term when they have different graphical structures. We then introduce an alternating direction method of multipliers (ADMM) based decomposition method for loss augmented inference, that only depends on two individual solvers for the loss function term and for the inference term as two independent subproblems. Second, we propose a novel surrogate loss function for submodular losses, the Lovász hinge, which leads to O(p log p) complexity with O(p) oracle accesses to the loss function to compute a subgradient or cutting-plane. Finally, we introduce a novel convex surrogate operator for general non-modular loss functions, which provides for the first time a tractable solution for loss functions that are neither supermodular nor submodular. This surrogate is based on a canonical submodular-supermodular decomposition
5
ALINEJAD, FARHAD. "Development of advanced criteria for blade root design and optimization." Doctoral thesis, Politecnico di Torino, 2018. http://hdl.handle.net/11583/2711560.
Full textAbstract:
In gas and steam turbine engines, blade root attachments are considered as critical components which require special attention for design. The traditional method of root design required high experienced engineers yet the strength of the material was not fully exploited in most cases. In the current thesis, different methodologies for automatic design and optimization of the blade root has been evaluated. Moreover, some methods for reducing the computational time have been proposed.
First, a simplified analytical model of the fir-tree was developed in order to evaluate mean stress in different sections of the blade root and disc groove. Then, a more detailed two-dimensional shape of the attachment capable to be analyzed in finite element (FE) analysis was developed for dovetail and fir-tree. The model was developed to be general in a way to include all possible shapes of the attachment. Then the projection of the analytical model over the 2D model was performed to compare the results obtained from analytical and FE methods. This comparison is essential in the later use of analytical evaluation of the fir-tree as a reduction technique of searching domain optimization.
Moreover, the possibility of predicting the contact normal stress of the blade and disc attachment by the use of a punch test was evaluated. A puncher composed of a flat surface and rounded edge was simulated equivalent to a sample case of a dovetail. The stress profile of the contact in analytical, 2d and 3d for puncher and dovetail was compared.
As an optimizer Genetic Algorithm (GA) was described and different rules affecting this algorithm was introduced. In order to reduce the number of callbacks to high fidelity finite element (FE) method, the surrogate functions were evaluated and among them, the Kriging function was selected to be constructed for use in the current study. Its efficiency was evaluated within a numerical optimization of a single lob. In this study, the surrogate model is not used solely in finding the optimum of the attachment shape as it may provide low accuracy but in order to benefit its fast evaluation and diminish its low accuracy drawback, the Kriging function (KRG) was used within GA as a pre-evaluation of the candidate before performing FE analysis. Moreover, the feasible and non-feasible space in a multi-dimensional complex searching domain of the attachment geometry is explained and also the challenge of a multi-district domain is tackled with a new mutation operation. In order to rectify the non-continuous domain, an adaptive penalty method based on Latin Hypercube Sampling (LHS) was proposed which could successfully improve the optimization convergence.
Furthermore, different topologies of the contact in a dovetail were assessed. Four different types of contact were modeled and optimized under the same loading and boundary conditions. The punch test was also assessed with different contact shapes. In addition, the state of stress for the dovetail in different rotational speed with different types of contact was assessed.
In the results and discussion, an optimization of a dovetail with the analytical approach was performed and the optimum was compared with the one obtained by FE analysis. It was found that the analytical approach has the advantage of fast evaluation and if constraints are well defined the results are comparable to the FE solution. Then, a Kriging function was embedded within the GA optimization and the approach was evaluated in an optimization of a dovetail. The results revealed that the low computational cost of the surrogate model is an advantage and the low accuracy would be diminished in a collaboration of FE and surrogate models. Later, the capability of employing the analytical approach in a fir-tree optimization is assessed. As the fir-tree geometry has a higher complexity working domain in comparison to the dovetail, the results would be consistent for the dovetail also. Different methods are assessed and compared. In the first attempt, the analytical approach was adopted as a filter to select out the least probable fit candidates. This method could provide a 7\% improvement in convergence. In another attempt, the proposed adaptive penalty method was added to the optimization which successfully found the reasonable optimum with 47\% reduction in computational cost. Later, a combination of analytical and FE models was joined in a multi-objective multi-level optimization which provided 32\% improvement with less error comparing to the previous method. In the last evaluation of this type, the analytical approach was solely used in a multi-objective optimization in which the results were selected according to an FE evaluation of most fit candidates. This approach although provided 86\% improvement in computational time reduction but it depends highly on the case under investigation and provides low accuracy in the final solution. Furthermore, a robust optimum was found for both dovetail and fir-tree in a multi-objective optimization. In this trial, the proposed adaptive penalty method in addition to the surrogate model was also involved.
6
Hinkle, Kurt Berlin. "An Automated Method for Optimizing Compressor Blade Tuning." BYU ScholarsArchive, 2016. https://scholarsarchive.byu.edu/etd/6230.
Full textAbstract:
Because blades in jet engine compressors are subject to dynamic loads based on the engine's speed, it is essential that the blades are properly "tuned" to avoid resonance at those frequencies to ensure safe operation of the engine. The tuning process can be time consuming for designers because there are many parameters controlling the geometry of the blade and, therefore, its resonance frequencies. Humans cannot easily optimize design spaces consisting of multiple variables, but optimization algorithms can effectively optimize a design space with any number of design variables. Automated blade tuning can reduce design time while increasing the fidelity and robustness of the design. Using surrogate modeling techniques and gradient-free optimization algorithms, this thesis presents a method for automating the tuning process of an airfoil. Surrogate models are generated to relate airfoil geometry to the modal frequencies of the airfoil. These surrogates enable rapid exploration of the entire design space. The optimization algorithm uses a novel objective function that accounts for the contribution of every mode's value at a specific operating speed on a Campbell diagram. When the optimization converges on a solution, the new blade parameters are output to the designer for review. This optimization guarantees a feasible solution for tuning of a blade. With 21 geometric parameters controlling the shape of the blade, the geometry for an optimally tuned blade can be determined within 20 minutes.
7
Tancred, James Anderson. "Aerodynamic Database Generation for a Complex Hypersonic Vehicle Configuration Utilizing Variable-Fidelity Kriging." University of Dayton / OhioLINK, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1543801033672049.
Full text
8
Guo, Xiao. "Bayesian surrogates for functional response modeling and metamaterial rapid design." HKBU Institutional Repository, 2017. http://repository.hkbu.edu.hk/etd_oa/418.
Full textAbstract:
In many scientific and engineering researches, Bayesian surrogate models are utilized to handle nonlinear data for regression and classification tasks. In this thesis, we consider a real-life problem, functional response modeling of metamaterial and its rapid design, to which we establish and test such models. To familiarize with this subject, some fundamental electromagnetic physics are provided.. Noticing that the dispersive data are usually in rational form, a two-stage modeling approach is proposed, where in the first stage, a universal link function is formulated to rationally approximate the data with a few discrete parameters, namely poles and residues. Then they are used to synthesize equivalent circuits, and surrogate models are applied to circuit elements in the second stage.. To start with a regression scheme, the classical Gaussian process (GP) is introduced, which proceeds by parameterizing a covariance function of any continuous inputs, and infers hyperparameters given the training data. Two metamaterial prototypes are illustrated to demonstrate the methodology of model building, whose results are shown to prove the efficiency and precision of probabilistic pre- dictions. One well-known problem with metamaterial functionality is its great variability in resonance identities, which shows discrepancy in approximation orders required to fit the data with rational functions. In order to give accurate prediction, both approximation order and the presenting circuit elements should be inferred, by classification and regression, respectively. An augmented Bayesian surrogate model, which integrates GP multiclass classification, Bayesian treed GP regression, is formulated to provide a systematic dealing to such unique physical phenomenon. Meanwhile, the nonstationarity and computational complexity are well scaled with such model.. Finally, as one of the most advantageous property of Bayesian perspective, probabilistic assessment to underlying uncertainties is also discussed and demonstrated with detailed formulation and examples.
9
Riley, Mike J. W. "Evaluating cascade correlation neural networks for surrogate modelling needs and enhancing the Nimrod/O toolkit for multi-objective optimisation." Thesis, Cranfield University, 2011. http://dspace.lib.cranfield.ac.uk/handle/1826/6796.
Full textAbstract:
Engineering design often requires the optimisation of multiple objectives, and becomes significantly more difficult and time consuming when the response surfaces are multimodal, rather than unimodal. A surrogate model, also known as a metamodel, can be used to replace expensive computer simulations, accelerating single and multi-objective optimisation and the exploration of new design concepts. The main research focus of this work is to investigate the use of a neural network surrogate model to improve optimisation of multimodal surfaces.
Several significant contributions derive from evaluating the Cascade Correlation neural network as the basis of a surrogate model. The contributions to the neural network community ultimately outnumber those to the optimisation community.
The effects of training this surrogate on multimodal test functions are explored. The Cascade Correlation neural network is shown to map poorly such response surfaces. A hypothesis for this weakness is formulated and tested. A new subdivision technique is created that addresses this problem; however, this new technique requires excessively large datasets upon which to train.
The primary conclusion of this work is that Cascade Correlation neural networks form an unreliable basis for a surrogate model, despite successes reported in the literature.
A further contribution of this work is the enhancement of an open source optimisation toolkit, achieved by the first integration of a truly multi-objective optimisation algorithm.
10
Wikström, Jonas. "3D Model of Fuel Tank for System Simulation : A methodology for combining CAD models with simulation tools." Thesis, Linköpings universitet, Maskinkonstruktion, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-71370.
Full textAbstract:
Engineering aircraft systems is a complex task. Therefore models and computer simulations are needed to test functions and behaviors of non existing systems, reduce testing time and cost, reduce the risk involved and to detect problems early which reduce the amount of implementation errors. At the section Vehicle Simulation and Thermal Analysis at Saab Aeronautics in Linköping every basic aircraft system is designed and simulated, for example the fuel system. Currently 2-dimensional rectangular blocks are used in the simulation model to represent the fuel tanks. However, this is too simplistic to allow a more detailed analysis. The model needs to be extended with a more complex description of the tank geometry in order to get a more accurate model. This report explains the different steps in the developed methodology for combining 3-dimensional geometry models of any fuel tank created in CATIA with dynamic simulation of the fuel system in Dymola. The new 3-dimensional representation of the tank in Dymola should be able to calculate fuel surface location during simulation of a maneuvering aircraft. The first step of the methodology is to create a solid model of the fuel contents in the tank. Then the area of validity for the model has to be specified, in this step all possible orientations of the fuel acceleration vector within the area of validity is generated. All these orientations are used in the automated volume analysis in CATIA. For each orientation CATIA splits the fuel body in a specified number of volumes and records the volume, the location of the fuel surface and the location of the center of gravity. This recorded data is then approximated with the use of radial basis functions implemented in MATLAB. In MATLAB a surrogate model is created which are then implemented in Dymola. In this way any fuel surface location and center of gravity can be calculated in an efficient way based on the orientation of the fuel acceleration vector and the amount of fuel. The new 3-dimensional tank model is simulated in Dymola and the results are compared with measures from the model in CATIA and with the results from the simulation of the old 2-dimensional tank model. The results shows that the 3-dimensional tank gives a better approximation of reality and that there is a big improvement compared with the 2-dimensional tank model. The downside is that it takes approximately 24 hours to develop this model.Att utveckla ett nytt flygplanssystem är en väldigt komplicerad arbetsuppgift. Därför används modeller och simuleringar för att testa icke befintliga system, minska utvecklingstiden och kostnaderna, begränsa riskerna samt upptäcka problem tidigt och på så sätt minska andelen implementerade fel. Vid sektionen Vehicle Simulation and Thermal Analysis på Saab Aeronautics i Linköping designas och simuleras varje grundflygplanssystem, ett av dessa system är bränslesystemet. För närvarande används 2-dimensionella rätblock i simuleringsmodellen för att representera bränsletankarna, vilket är en väldigt grov approximation. För att kunna utföra mer detaljerade analyser behöver modellerna utökas med en bättre geometrisk beskrivning av bränsletankarna. Denna rapport går igenom de olika stegen i den framtagna metodiken för att kombinera 3- dimensionella tankmodeller skapade i CATIA med dynamisk simulering av bränslesystemet i Dymola. Den nya 3-dimensionella representationen av en tank i Dymola bör kunna beräkna bränsleytans läge under en simulering av ett manövrerande flygplan. Första steget i metodiken är att skapa en solid modell av bränslet som finns i tanken. Därefter specificeras modellens giltighetsområde och alla tänkbara riktningar hos accelerationsvektorn som påverkar bränslet genereras, dessa används sedan i den automatiserade volymanalysen i CATIA. För varje riktning delar CATIA upp bränslemodellen i ett bestämt antal delar och registrerar volymen, bränsleytans läge samt tyngdpunktens position för varje del. Med hjälp av radiala basfunktioner som har implementerats i MATLAB approximeras dessa data och en surrogatmodell tas fram, denna implementeras sedan i Dymola. På så sätt kan bränsleytans och tyngdpunktens läge beräknas på ett effektivt sätt, baserat på riktningen hos bränslets accelerationsvektor samt mängden bränsle i tanken. Den nya 3-dimensionella tankmodellen simuleras i Dymola och resultaten jämförs med mätningar utförda i CATIA samt med resultaten från den gamla simuleringsmodellen. Resultaten visar att den 3-dimensionella tankmodellen ger en mycket bättre representation av verkligheten och att det är en stor förbättring jämfört med den 2-dimensionella representationen. Nackdelen är att det tar ungefär 24 timmar att få fram denna 3-dimensionella representation.
Books on the topic "Surrogate Function"
1
International Falk Workshop (1995 Basel, Switzerland). Surrogate markers to assess efficacy of treatmentin chronic liver diseases: Proceedings of the International Falk Workshop held in Basel, Switzerland, October 23-24, 1995. Dordrecht: Kluwer Academic, 1996.
Find full text
2
J, Booker Andrew, and Institute for Computer Applications in Science and Engineering., eds. A rigorous framework for optimization of expensive functions by surrogates. Hampton, VA: Institute for Computer Applications in Science and Engineering, NASA Langley Research Center, 1998.
Find full text
3
Fong, Siao Yuong. Performing Fear in Television Production. Nieuwe Prinsengracht 89 1018 VR Amsterdam Nederland: Amsterdam University Press, 2022. http://dx.doi.org/10.5117/9789463724579.
Full textAbstract:
What goes into the ideological sustenance of an illiberal capitalist democracy? While much of the critical discussion of the media in authoritarian contexts focus on state power, the emphasis on strong states tend to perpetuate misnomers about the media as mere tools of the state and sustain myths about their absolute power. Turning to the lived everyday of media producers in Singapore, I pose a series of questions that explore what it takes to perpetuate authoritarian resilience in the mass media. How, in what terms and through what means, does a politically stable illiberal Asian state like Singapore formulate its dominant imaginary of social order? What are the television production practices that perform and instantiate the social imaginary, and who are the audiences that are conjured and performed in the process? What are the roles played by imagined audiences in sustaining authoritarian resilience in the media? If, as I will argue in the book, audiences function as the central problematic that engenders anxieties and self-policing amongst producers, can the audience become a surrogate for the authoritarian state?
4
Hall, Peter. Principal component analysis for functional data. Edited by Frédéric Ferraty and Yves Romain. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780199568444.013.8.
Full textAbstract:
This article discusses the methodology and theory of principal component analysis (PCA) for functional data. It first provides an overview of PCA in the context of finite-dimensional data and infinite-dimensional data, focusing on functional linear regression, before considering the applications of PCA for functional data analysis, principally in cases of dimension reduction. It then describes adaptive methods for prediction and weighted least squares in functional linear regression. It also examines the role of principal components in the assessment of density for functional data, showing how principal component functions are linked to the amount of probability mass contained in a small ball around a given, fixed function, and how this property can be used to define a simple, easily estimable density surrogate. The article concludes by explaining the use of PCA for estimating log-density.
5
Ince, Can, and Alexandre Lima. Monitoring the microcirculation in the ICU. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780199600830.003.0142.
Full textAbstract:
The microcirculation is the key physiological compartment of the cardiovascular system where oxygen is delivered by convection and diffusion to respiring parenchymal cells to support cellular, and thereby organ, function. The microcirculation consists of microvessels less than 100 µmin diameter consisting of arterioles, capillaries, and venules. The smallest vessels (<6 µm) are the capillaries where most oxygen leaves the circulation by passive diffusion to cells. The critical role of the microcirculation has long been recognized, although it has recently been possible to image its function at the bedside, thus making it a clinically important compartment to monitor. Prior to this type of monitoring, peripheral perfusion was used as a surrogate before more advanced optical techniques were developed to image microcirculatory function both non-invasively and at the bedside. This chapter provides a brief overview of microcirculatory assessment.
6
Shanley, Mary Lyndon. Surrogacy. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198786429.003.0015.
Full textAbstract:
The development of assisted-reproductive technologies sharpened perceptions of the differences among three major criteria for parental status—biological (genetics and gestation), volition/intention, and caregiving/functional. This chapter surveys the development of these justifications. It argues that of these, caregiving—and the underlying philosophic framework of the ethics of care—is the most satisfactory grounding of parental status for three reasons: first, it places relationship at the centre of its theoretical and practical concerns; second, caregiving focuses attention on the child; and third, thinking about relationships of care ensures that we consider the impact of social factors, such as race and class, on reproduction and family formation. But despite its strengths, this chapter concludes that caregiving is not fully satisfactory for grounding recognition of a parent–child relationship. It advocates a pluralistic account that regards the relationships established by all three criteria, as significant to both social and legal groundings of parental status.
7
Kulkarni, Kunal, James Harrison, Mohamed Baguneid, and Bernard Prendergast, eds. Trauma and orthopaedics. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198729426.003.0031.
Full textAbstract:
Recent advances in biomechanics and biomaterials are resulting in new and potentially improved implants and procedures in trauma medicine, often with more reliance on high-tech solutions. However, some new advances have resulted in disastrous outcomes. As it takes time for these complications to surface, many patients may be subject to the new technology and resulting consequences. Studying the clinical evidence around these technologies is therefore essential, and use of appropriate surrogate measures to assess the short-term in vivo performance of an implant is important to help predict long-term clinical outcome. Radiostereometric analysis and kinematic assessment are two such tools widely used in translational research and post-market surveillance in the field of joint replacement. It is only with high-quality research and awareness that true advances can be demonstrated and failures averted at the earliest stage. The principles of orthopaedics must remain to alleviate pain, correct deformity, and restore function, whatever technique is used.
8
Sidhu, Kulraj S., Mfonobong Essiet, and Maxime Cannesson. Cardiac and vascular physiology in anaesthetic practice. Edited by Jonathan G. Hardman. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780199642045.003.0001.
Full textAbstract:
This chapter discusses key components of cardiovascular physiology applicable to clinical practice in the field of anaesthesiology. From theory development to ground-breaking innovations, the history of cardiac and vascular anatomy, as well as physiology, is presented. Utilizing knowledge of structure and function, parameters created have allowed adequate patient clinical assessment and guided interventions. A review of concepts reveals the impact of multiple physiological variables on a patient’s haemodynamic state and the need for more accurate and efficient measurements. In particular, it is noted that a more reliable index of ventricular contractility is the end-systolic elastance rather than the ejection fraction. Constant direct preload assessment has not yet been achieved but continues to be determined through surrogate variables, and continuous cardiac output monitoring for oxygen delivery, although advancing, has limitations. Considering the effect of compound factors perioperatively, especially heart failure, modifies the goals and interventions of anaesthetists to achieve improved outcomes. Therefore, medical management prior to surgery and complete assessment through history, physical examination, and diagnostic tests are a priority. This chapter also details the expectations following volume expansion to augment haemodynamics during surgery, the concept of functional haemodynamic monitoring, and limitations to the parameters applied in assessing fluid responsiveness. Challenging the accuracy of conventional indices to predict volume status led to the use of goal-directed therapy, reducing morbidity and minimizing length of hospital stay. The mainstay of this chapter is to reinforce the relevance of advances in haemodynamic monitoring and homeostasis optimization by anaesthetists during surgery, using fundamental concepts of cardiovascular physiology.
9
Pitzalis, Costantino, Frances Humby, and Michael P. Seed. Synovial pathology. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780199642489.003.0052.
Full textAbstract:
Synovial pathology is seen in a variety of disease states, including rheumatoid arthritis (RA), osteoarthritis (OA), psoriatic arthritis, and systemic lupus erythmatosus (SLE). This chapter highlights recent advances that characterize the cellular composition of these tissues according to surface markers and chemokine and cytokine expression, and describes synovial functional status and response to therapeutics. In RA, after initiation, pannus migrates over and under cartilage, and into subchondral bone, in a destructive process. Cartilage-pannus junction (CPJ) is characterized as invasive or 'quiescent' or 'indistinct'. Invasive CPJ can comprise macrophages, fibroblast-like synoviocytes (FLS), mast cells, and/or neutrophils. CPJ activity is related to the state of activation of the overlying subintima. Subintimal inflammation can be graded to a variety of degrees (I–IV) according to established criteria and is illustrated. In some RA synovia, cellular aggregates organize into ectopic lymphoid structures (ELS) through the expression of lymphorganogenic signals, to exhibit T- or B-cell zones accompanied by dendritic cells and lymphangiogenesis. ELS synthesize rheumatoid factor (RF) and anti-citrullinated peptide antibodies (ACAP), considered to be indicative of aggressive disease. The selective cellular expression of macrophage and dendritic cell chemokines and cytokines such as TNF, GMCSF, TGFβ, IL-1, IL-6, IL-23, and chemokines can be seen in synovia, to form a regulated and cooperative environment that sustains the cellular organization and pathological function. Important to this process are FLS and CD68+ macrophages. CD68 expression correlates with disease severity and can be useful as a surrogate marker of disease modifying activity of therapeutics, such as anti-TNF and anti-B-cell biologics.
10
Seeck, Margitta, L. Spinelli, Jean Gotman, and Fernando H. Lopes da Silva. Combination of Brain Functional Imaging Techniques. Edited by Donald L. Schomer and Fernando H. Lopes da Silva. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190228484.003.0046.
Full textAbstract:
Several tools are available to map brain electrical activity. Clinical applications focus on epileptic activity, although electric source imaging (ESI) and electroencephalography-coupled functional magnetic resonance imaging (EEG–fMRI) are also used to investigate non-epileptic processes in healthy subjects. While positron-emission tomography (PET) reflects glucose metabolism, strongly linked with synaptic activity, and single-photon-emission computed tomography (SPECT) reflects blood flow, fMRI (BOLD) signals have a hemodynamic component that is a surrogate signal of neuronal (synaptic) activity. The exact interpretation of BOLD signals is not completely understood; even in unifocal epilepsy, more than one region of positive or negative BOLD is often observed. Co-registration of medical images is essential to answer clinical questions, particularly for presurgical epilepsy evaluations. Multimodal imaging can yield information about epileptic foci and underlying networks. Co-registering MRI, PET, SPECT, fMRI, and ESI (or magnetic source imaging) provides information to estimate the epileptogenic zone and can help optimize surgical results.
Book chapters on the topic "Surrogate Function"
1
Abbasnejad, Amir, and Dirk V. Arnold. "Adaptive Function Value Warping for Surrogate Model Assisted Evolutionary Optimization." In Lecture Notes in Computer Science, 76–89. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-14714-2_6.
Full text
2
Yang, Kaifeng, and Michael Affenzeller. "Surrogate-assisted Multi-objective Optimization via Genetic Programming Based Symbolic Regression." In Lecture Notes in Computer Science, 176–90. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-27250-9_13.
Full textAbstract:
AbstractSurrogate-assisted optimization algorithms are a commonly used technique to solve expensive-evaluation problems, in which a regression model is built to replace an expensive function. In some acquisition functions, the only requirement for a regression model is the predictions. However, some other acquisition functions also require a regression model to estimate the “uncertainty” of the prediction, instead of merely providing predictions. Unfortunately, very few statistical modeling techniques can achieve this, such as Kriging/Gaussian processes, and recently proposed genetic programming-based (GP-based) symbolic regression with Kriging (GP2). Another method is to use a bootstrapping technique in GP-based symbolic regression to estimate prediction and its corresponding uncertainty. This paper proposes to use GP-based symbolic regression and its variants to solve multi-objective optimization problems (MOPs), which are under the framework of a surrogate-assisted multi-objective optimization algorithm (SMOA). Kriging and random forest are also compared with GP-based symbolic regression and GP2. Experiment results demonstrate that the surrogate models using the GP2 strategy can improve SMOA’s performance.
3
Cao, Xin, Chao Jiang, and Anjun Zu. "Uncertainty analysis of dam finite element simulation based on a surrogate model." In Advances in Civil Function Structure and Industrial Architecture, 594–600. London: CRC Press, 2022. http://dx.doi.org/10.1201/9781003305019-82.
Full text
4
Wang, Xi, Hao Chen, Luyang Luo, An-ran Ran, Poemen P. Chan, Clement C. Tham, Carol Y. Cheung, and Pheng-Ann Heng. "Unifying Structure Analysis and Surrogate-Driven Function Regression for Glaucoma OCT Image Screening." In Lecture Notes in Computer Science, 39–47. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32239-7_5.
Full text
5
Zhang, Chengfang, and Xingchun Yang. "Image Fusion Based on Masked Online Convolutional Dictionary Learning with Surrogate Function Approach." In Advances in Intelligent Systems and Computing, 70–74. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-5887-0_10.
Full text
6
Dhadve, Ajit, Bhushan Thakur, and Pritha Ray. "Dual Modality Imaging of Promoter Activity as a Surrogate for Gene Expression and Function." In Methods in Molecular Biology, 1–12. New York, NY: Springer New York, 2018. http://dx.doi.org/10.1007/978-1-4939-7860-1_1.
Full text
7
Domesová, Simona. "The Use of Radial Basis Function Surrogate Models for Sampling Process Acceleration in Bayesian Inversion." In Lecture Notes in Electrical Engineering, 228–38. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-14907-9_23.
Full text
8
Diestmann, Thomas, Nils Broedling, Benedict Götz, and Tobias Melz. "Surrogate Model-Based Uncertainty Quantification for a Helical Gear Pair." In Lecture Notes in Mechanical Engineering, 191–207. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-77256-7_16.
Full textAbstract:
AbstractCompetitive industrial transmission systems must perform most efficiently with reference to complex requirements and conflicting key performance indicators. This design challenge translates into a high-dimensional multi-objective optimization problem that requires complex algorithms and evaluation of computationally expensive simulations to predict physical system behavior and design robustness. Crucial for the design decision-making process is the characterization, ranking, and quantification of relevant sources of uncertainties. However, due to the strict time limits of product development loops, the overall computational burden of uncertainty quantification (UQ) may even drive state-of-the-art parallel computing resources to their limits. Efficient machine learning (ML) tools and techniques emphasizing high-fidelity simulation data-driven training will play a fundamental role in enabling UQ in the early-stage development phase.This investigation surveys UQ methods with a focus on noise, vibration, and harshness (NVH) characteristics of transmission systems. Quasi-static 3D contact dynamic simulations are performed to evaluate the static transmission error (TE) of meshing gear pairs under different loading and boundary conditions. TE indicates NVH excitation and is typically used as an objective function in the early-stage design process. The limited system size allows large-scale design of experiments (DoE) and enables numerical studies of various UQ sampling and modeling techniques where the design parameters are treated as random variables associated with tolerances from manufacturing and assembly processes. The model accuracy of generalized polynomial chaos expansion (gPC) and Gaussian process regression (GPR) is evaluated and compared. The results of the methods are discussed to conclude efficient and scalable solution procedures for robust design optimization.
9
Mehta, Arpan R., Siddharthan Chandran, and Bhuvaneish T. Selvaraj. "Assessment of Mitochondrial Trafficking as a Surrogate for Fast Axonal Transport in Human Induced Pluripotent Stem Cell–Derived Spinal Motor Neurons." In Methods in Molecular Biology, 311–22. New York, NY: Springer US, 2022. http://dx.doi.org/10.1007/978-1-0716-1990-2_16.
Full textAbstract:
AbstractAxonal transport is essential for the development, function, and survival of the nervous system. In an energy-demanding process, motor proteins act in concert with microtubules to deliver cargoes, such as organelles, from one end of the axon to the other. Perturbations in axonal transport are a prominent phenotype of many neurodegenerative diseases, including amyotrophic lateral sclerosis. Here, we describe a simple method to fluorescently label mitochondrial cargo, a surrogate for fast axonal transport, in human induced pluripotent stem cell–derived motor neurons. This method enables the sparse labeling of axons to track directionality of movement and can be adapted to assess not only the cell autonomous effects of a genetic mutation on axonal transport but also the cell non-autonomous effects, through the use of conditioned medium and/or co-culture systems.
10
Lakshika, Erandi, Michael Barlow, and Adam Easton. "Evolving High Fidelity Low Complexity Sheepdog Herding Simulations Using a Machine Learner Fitness Function Surrogate for Human Judgement." In AI 2015: Advances in Artificial Intelligence, 330–42. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26350-2_29.
Full textConference papers on the topic "Surrogate Function"
1
Qian, Jiachang, Enen Yu, Jinlan Zhang, Dawei Zhan, and Yuansheng Cheng. "Optimization of the Vibration Response of a Longitudinal-Transverse Stiffened Conical Shell Based on an Ensemble of Surrogates." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-77334.
Full textAbstract:
The acceleration responses at certain points of the longitudinal-transverse stiffened conical shells in special frequency region are major matters of concern. Because the finite element models of the longitudinal-transverse stiffened conical shells have to be employed to calculate the vibration response of the structure at all frequencies under consideration, it requires a large amount of computational cost when the optimization is conducted. In order to optimize the vibration response of the longitudinal-transverse stiffened conical shell, the surrogate modeling method is used in this study to approximate the frequency-acceleration response function which makes the vibration response optimization affordable. Since different surrogate models often perform differently in different regions of the design space, an ensemble of surrogate models is utilized to maximize the overall accuracy over the whole design space. The ensemble of surrogates is a weighted combination of Kriging model, radial basis function (RBF) and support vector regression (SVR). The weights of the ensemble of surrogates vary in different regions and are determined by the estimated errors of the surrogate models at the study point. The smaller the estimated error is, the higher the weight is. Then the prediction of ensemble of surrogates is compared to the individual surrogate’s, and the results show that the accuracies of the ensemble of surrogates in peak regions are significant higher than its components. Based on the ensemble of surrogates, a vibration optimization of a longitudinal-transverse stiffened conical shell is conducted using genetic algorithm (GA). The design variables of the optimization are the thickness of the longitudinal-transverse stiffened conical shell and the height of stiffened structure. The objective is to minimize the highest acceleration of the shell and the calculations of the peak accelerations are approximated by the built ensemble of the surrogates. The constraints include the weight of the stiffened conical shell and structure size combination. The optimization results show that the proposed approach is efficient in optimization of the vibration response of longitudinal-transverse stiffened conical shells.
2
Raphel, Mariya, Revati Gunjal, S. R. Wagh, and N. M. Singh. "Optimization of Surrogate function using Extremum Seeking Control." In 2022 Australian & New Zealand Control Conference (ANZCC). IEEE, 2022. http://dx.doi.org/10.1109/anzcc56036.2022.9966972.
Full text
3
Albert, Christopher G., Ulrich Callies, and Udo von Toussaint. "Surrogate-Enhanced Parameter Inference for Function-Valued Models." In International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. Basel Switzerland: MDPI, 2021. http://dx.doi.org/10.3390/psf2021003011.
Full text
4
Kamenik, Jan, Michele Stramacchia, David J. J. Toal, Andy J. Keane, and Ron Bates. "Axial Compressor Rotor Optimization Using a Novel Ensemble of Surrogates-Based Infill Criterion." In ASME 2017 Gas Turbine India Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gtindia2017-4516.
Full textAbstract:
A novel infill criterion for so-called ensemble of surrogates-based optimization is proposed and applied in practice for an aerodynamic compressor rotor design optimization. The ensemble uses a combined approach based on different radial basis functions and aims to reduce prediction errors through weighted linear combinations of radial basis functions. The update strategy uses a new hybrid custom metric termed α, which incorporates information about each surrogate’s local agreement through correlation coefficients and also information about the global accuracy of each ensemble combination through the root-mean-square error. Surrogate models are searched using a hybrid optimizer, i.e., with a genetic algorithm and sequential quadratic programming, and proposed update points are evaluated using the high-fidelity black box function. The results are compared with established optimization approaches and the best design is analyzed further in terms of the flow physics. Results show that α-based ensemble of surrogates approaches are particularly efficient for large-scale cases, where other types of surrogates such as Kriging models are onerous to construct.
5
Penmetsa, Ravi, and Ramana Grandhi. "Estimating Membership Function of Implicit Response Using Surrogate Models." In 43rd AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2002. http://dx.doi.org/10.2514/6.2002-1234.
Full text
6
Alicino, Simone, and Massimiliano Vasile. "Surrogate-based Maximisation of Belief Function for Robust Design Optimisation." In 54th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2013. http://dx.doi.org/10.2514/6.2013-1757.
Full text
7
Talgorn, Bastien, Sébastien Le Digabel, and Michael Kokkolaras. "Problem Formulations for Simulation-Based Design Optimization Using Statistical Surrogates and Direct Search." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34778.
Full textAbstract:
Typical challenges of simulation-based design optimization include unavailable gradients and unreliable approximations thereof, expensive function evaluations, numerical noise, multiple local optima and the failure of the analysis to return a value to the optimizer. The remedy for all these issues is to use surrogate models in lieu of the computational models or simulations and derivative-free optimization algorithms. In this work, we use the R dynaTree package to build statistical surrogates of the blackboxes and the direct search method for derivative-free optimization. We present different formulations for the surrogate problem considered at each search step of the Mesh Adaptive Direct Search (MADS) algorithm using a surrogate management framework. The proposed formulations are tested on two simulation-based multidisciplinary design optimization problems. Numerical results confirm that the use of statistical surrogates in MADS improves the efficiency of the optimization algorithm.
8
Shen, Yichi, and Christine A. Shoemaker. "Global Optimization for Noisy Expensive Black-Box Multi-Modal Functions Via Radial Basis Function Surrogate." In 2020 Winter Simulation Conference (WSC). IEEE, 2020. http://dx.doi.org/10.1109/wsc48552.2020.9384132.
Full text
9
Valladares, Homero, and Andres Tovar. "A Simple and Effective Methodology to Perform Multi-Objective Bayesian Optimization: An Application in the Design of Sandwich Composite Armors for Blast Mitigation." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22564.
Full textAbstract:
Abstract Bayesian optimization is a versatile numerical method to solve global optimization problems of high complexity at a reduced computational cost. The efficiency of Bayesian optimization relies on two key elements: a surrogate model and an acquisition function. The surrogate model is generated on a Gaussian process statistical framework and provides probabilistic information of the prediction. The acquisition function, which guides the optimization, uses the surrogate probabilistic information to balance the exploration and the exploitation of the design space. In the case of multi-objective problems, current implementations use acquisition functions such as the multi-objective expected improvement (MEI). The evaluation of MEI requires a surrogate model for each objective function. In order to expand the Pareto front, such implementations perform a multi-variate integral over an intricate hypervolume, which require high computational cost. The objective of this work is to introduce an efficient multi-objective Bayesian optimization method that avoids the need for multi-variate integration. The proposed approach employs the working principle of multi-objective traditional methods, e.g., weighted sum and min-max methods, which transform the multi-objective problem into a single-objective problem through a functional mapping of the objective functions. Since only one surrogate is trained, this approach has a low computational cost. The effectiveness of the proposed approach is demonstrated with the solution of four problems: (1) an unconstrained version of the Binh and Korn test problem (convex Pareto front), (2) the Fonseca and Fleming test problem (non-convex Pareto front), (3) a three-objective test problem and (4) the design optimization of a sandwich composite armor for blast mitigation. The optimization algorithm is implemented in MATLAB and the finite element simulations are performed in the explicit, nonlinear finite element analysis code LS-DYNA. The results are comparable (or superior) to the results of the MEI acquisition function.
10
Mathieson, James L., Aravind Shanthakumar, Chiradeep Sen, Ryan Arlitt, Joshua D. Summers, and Robert Stone. "Complexity as a Surrogate Mapping Between Function Models and Market Value." In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2011. http://dx.doi.org/10.1115/detc2011-47481.
Full textAbstract:
The purpose of this paper is to investigate if early stage function models of design can be used to predict the market-value of a commercial product. In previous research, several metrics of complexity of graph-based product models have been proposed and suitably chosen combinations of these metrics have been shown to predict the time required in assembling commercial products. By extension, this research investigates if this approach, using new sets of combinations of complexity metrics, can predict market-value. To this end, the complexity values of function structures for eighteen products from the Design Repository are determined from their function structure graphs, while their market values are procured from different vendor quotes in the open market. The complexity and value information for fourteen samples are used to train a neural net program to define a predictive mapping scheme. This program is then used to predict the value of the final four products. The results of this approach demonstrate that complexity metrics can be used as inputs to neural networks to establish an accurate mapping from function structure design representations to market values to within the distribution of values for products of similar type.
Reports on the topic "Surrogate Function"
1
Pettit, Chris, and D. Wilson. A physics-informed neural network for sound propagation in the atmospheric boundary layer. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41034.
Full textAbstract:
We describe what we believe is the first effort to develop a physics-informed neural network (PINN) to predict sound propagation through the atmospheric boundary layer. PINN is a recent innovation in the application of deep learning to simulate physics. The motivation is to combine the strengths of data-driven models and physics models, thereby producing a regularized surrogate model using less data than a purely data-driven model. In a PINN, the data-driven loss function is augmented with penalty terms for deviations from the underlying physics, e.g., a governing equation or a boundary condition. Training data are obtained from Crank-Nicholson solutions of the parabolic equation with homogeneous ground impedance and Monin-Obukhov similarity theory for the effective sound speed in the moving atmosphere. Training data are random samples from an ensemble of solutions for combinations of parameters governing the impedance and the effective sound speed. PINN output is processed to produce realizations of transmission loss that look much like the Crank-Nicholson solutions. We describe the framework for implementing PINN for outdoor sound, and we outline practical matters related to network architecture, the size of the training set, the physics-informed loss function, and challenge of managing the spatial complexity of the complex pressure.
2
Dennis, John E., Audet Jr, and Charles. Optimization Tools for Engineering Design Using Surrogate Functions. Fort Belvoir, VA: Defense Technical Information Center, February 2004. http://dx.doi.org/10.21236/ada420453.
Full text
3
Dennis, John E., Moore Jr., and Douglas. Optimization Tools for Engineering Design Using Surrogate Functions. Fort Belvoir, VA: Defense Technical Information Center, November 2000. http://dx.doi.org/10.21236/ada387716.
Full text
4
Dennis, John E., and Jr. New Meta Algorithms for Engineering Design Using Surrogate Functions. Fort Belvoir, VA: Defense Technical Information Center, April 2005. http://dx.doi.org/10.21236/ada433179.
Full text
5
Dennis, J. E., and Virginia Torczon. Managing the Choice of Surrogate Variables and the Use of Approximation Models to Optimize Expensive Functions. Fort Belvoir, VA: Defense Technical Information Center, May 1998. http://dx.doi.org/10.21236/ada380051.
Full text
6
Allen, Luke, Joon Lim, Robert Haehnel, and Ian Dettwiller. Helicopter rotor blade multiple-section optimization with performance. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41031.
Full textAbstract:
This paper presents advancements in a surrogate-based, rotor blade design optimization framework for improved helicopter performance. The framework builds on previous successes by allowing multiple airfoil sections to designed simultaneously to minimize required rotor power in multiple flight conditions. Rotor power in hover and forward flight, at advance ratio 𝜇 = 0.3, are used as objective functions in a multi-objective genetic algorithm. The framework is constructed using Galaxy Simulation Builder with optimization provided through integration with Dakota. Three independent airfoil sections are morphed using ParFoil and aerodynamic coefficients for the updated airfoil shapes (i.e., lift, drag, moment) are calculated using linear interpolation from a database generated using C81Gen/ARC2D. Final rotor performance is then calculated using RCAS. Several demonstrative optimization case studies were conducted using the UH-60A main rotor. The degrees of freedom for this case are limited to the airfoil camber, camber crest position, thickness, and thickness crest position for each of the sections. The results of the three-segment case study show improvements in rotor power of 4.3% and 0.8% in forward flight and hover, respectively. This configuration also yields greater reductions in rotor power for high advance ratios, e.g., 6.0% reduction at 𝜇 = 0.35, and 8.8% reduction at 𝜇 = 0.4.
7
Zhao, Bingyu, Saul Burdman, Ronald Walcott, and Gregory E. Welbaum. Control of Bacterial Fruit Blotch of Cucurbits Using the Maize Non-Host Disease Resistance Gene Rxo1. United States Department of Agriculture, September 2013. http://dx.doi.org/10.32747/2013.7699843.bard.
Full textAbstract:
The specific objectives of this BARD proposal were: (1) To determine whether Rxol can recognize AacavrRxo1 to trigger BFB disease resistance in stable transgenic watermelon plants. (2) To determine the distribution of Aac-avrRxo1 in a global population of Aae and to characterize the biological function of Aac-avrRxo1. (3) To characterize other TIS effectors of Aae and to identify plant R gene(s) that can recognize conserved TIS effectors of this pathogen. Background to the topic: Bacterial fruit blotch (BFB) of cucurbits, caused by Acidovorax avenae subsp. citrulli (Aae), is a devastating disease that affects watermelon (Citrullus lanatus) and melon (Cucumis melo) production worldwide, including both Israel and USA. Two major groups of Aae strains have been classified based on their virulence on host plants, genetics and biochemical properties. Thus far, no effective resistance genes have been identified from cucurbit germplasm. In this project, we assessed the applicability of a non-host disease resistance gene, Rxol, to control BFB in watermelon. We also tried to identify Aae type III secreted (TIS) effectors that can be used as molecular probes to identify novel disease resistance genes in both cucurbits and Nieotianatabaeum. Major conclusions, solutions, achievements: We generated five independent transgenic watermelon (cv. Sugar Babay) plants expressing the Rxol gene. The transgenic plants were evaluated with Aae strains AAC001 and M6 under growth chamber conditions. All transgenic plants were found to be susceptible to both Aae strains. It is possible that watermelon is missing other signaling components that are required for Rxol-mediated disease resistance. In order to screen for novel BFB resistance genes, we inoculated two Aae strains on 60 Nieotiana species. Our disease assay revealed Nicotiana tabaeum is completely resistant to Aae, while its wild relative N. benthamiana is susceptible to Aae. We further demonstrated that Nieotiana benthamiana can be used as a surrogate host for studying the mechanisms of pathogenesis of Aae. We cloned 11 TIS effector genes including the avrRxolhomologues from the genomes of 22 Aae strains collected worldwide. Sequencing analysis revealed that functional avrRxol is conserved in group" but not group I Aae strains. Three effector genes- Aave_1548, Aave_2166 and Aave_2708- possessed the ability to trigger an HR response in N. tabacum when they were transiently expressed by Agrobaeterium. We conclude that N. tabacum carries at least three different non-host resistance genes that can specifically recognize AaeTIS effectors to trigger non-host resistance. Screening 522 cucurbits genotypes with two Aae strains led us to identify two germplasm (P1536473 and P1273650) that are partially resistant to Aae. Interestingly, transient expression of the TIS effector, Aave_1548, in the two germplasms also triggered HR-Iike cell death, which suggests the two lines may carry disease resistance genes that can recognize Aave_1548. Importantly, we also demonstrated that this effector contributes to the virulence of the bacterium in susceptible plants. Therefore, R genes that recognize effector Aave1548 have great potential for breeding for BFB resistance. To better understand the genome diversity of Aae strains, we generated a draft genome sequence of the Israeli Aae strain, M6 (Group I) using Iliumina technology. Comparative analysis of whole genomes of AAC001, and M6 allowed us to identify several effectors genes that differentiate groups I and II. Implications, both scientific and agricultural: The diversity of TIS effectors in group I and II strains of Aae suggests that a subset of effectors could contribute to the host range of group I and II Aae strains. Analysis of these key effectors in a larger Aae population may allow us to predict which cucurbit hosts may be at risk to BFB. Additionally, isolation of tobacco and cucurbit Rgenes that can recognize Aae type III effectors may offer new genetic resources for controlling BFB.
8
Selvaraju, Ragul, Hari Shankar, and Hariharan Sankarasubramanian. Metamodel Generation for Frontal Crash Scenario of a Passenger Car. SAE International, September 2020. http://dx.doi.org/10.4271/2020-28-0504.
Full textAbstract:
A frontal impact scenario was simulated using a Finite Element Model of a Hybrid III 50th percentile male (LSTC, Livermore CA) along with seatbelt, steering system and driver airbags. The boundary conditions included acceleration pulse to the seat and the outputs including injury measures in terms of Head Injury Criterion (HIC), Normalized Neck Injury Criterion (NIJ) and Chest Severity Index (CSI) were extracted from the simulations. The kinematics of the Hybrid III were validated against the kinematics of post mortem human surrogates (PMHS) available in the literature. Using the validated setup, metamodels were generated by creating a design of varying different parameters and recording the responses for each design. First, the X and Z translation of dummy along the seat is provided as input for which there was no variation in the head injury criterion (HIC). Next, the input pulse to the seat is parameterized along with the seatbelt loading and the results are obtained respectively. The outputs, in terms of injury measures, are generated in the form of metamodels as a function of the parameters. The occupant model used for the frontal crash scenario in LS-Dyna is validated against the previously available crash experimental data. A total of 100 design points was generated with a varying combination of parameters. An increase in various injury measures was observed with an increase in the scale factor of the acceleration pulse. Also, it was found that chest severity index increased with an increase in the scale factor of the seat belt loading force.
9
Selvaraju, Ragul, Hari Shankar, and Hariharan Sankarasubramanian. Metamodel Generation for Frontal Crash Scenario of a Passenger Car. SAE International, September 2020. http://dx.doi.org/10.4271/2020-28-0504.
Full textAbstract:
A frontal impact scenario was simulated using a Finite Element Model of a Hybrid III 50th percentile male (LSTC, Livermore CA) along with seatbelt, steering system and driver airbags. The boundary conditions included acceleration pulse to the seat and the outputs including injury measures in terms of Head Injury Criterion (HIC), Normalized Neck Injury Criterion (NIJ) and Chest Severity Index (CSI) were extracted from the simulations. The kinematics of the Hybrid III were validated against the kinematics of post mortem human surrogates (PMHS) available in the literature. Using the validated setup, metamodels were generated by creating a design of varying different parameters and recording the responses for each design. First, the X and Z translation of dummy along the seat is provided as input for which there was no variation in the head injury criterion (HIC). Next, the input pulse to the seat is parameterized along with the seatbelt loading and the results are obtained respectively. The outputs, in terms of injury measures, are generated in the form of metamodels as a function of the parameters. The occupant model used for the frontal crash scenario in LS-Dyna is validated against the previously available crash experimental data. A total of 100 design points was generated with a varying combination of parameters. An increase in various injury measures was observed with an increase in the scale factor of the acceleration pulse. Also, it was found that chest severity index increased with an increase in the scale factor of the seat belt loading force.