Relevant bibliographies by topics / Multifidelity models

Academic literature on the topic 'Multifidelity models'

Author: Grafiati
Published: 7 July 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers
  5. Reports

Journal articles on the topic "Multifidelity models":

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Molléro, Roch, Xavier Pennec, Hervé Delingette, Alan Garny, Nicholas Ayache, and Maxime Sermesant. "Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models." Biomechanics and Modeling in Mechanobiology 17, no. 1 (September 11, 2017): 285–300. http://dx.doi.org/10.1007/s10237-017-0960-0.

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Jacobs, Ryan, Philip E. Goins, and Dane Morgan. "Role of multifidelity data in sequential active learning materials discovery campaigns: case study of electronic bandgap." Machine Learning: Science and Technology 4, no. 4 (December 1, 2023): 045060. http://dx.doi.org/10.1088/2632-2153/ad1627.

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Abstract Materials discovery and design typically proceeds through iterative evaluation (both experimental and computational) to obtain data, generally targeting improvement of one or more properties under one or more constraints (e.g. time or budget). However, there can be great variation in the quality and cost of different data, and when they are mixed together in what we here call multifidelity data, the optimal approaches to their utilization are not established. It is therefore important to develop strategies to acquire and use multifidelity data to realize the most efficient iterative materials exploration. In this work, we assess the impact of using multifidelity data through mock demonstration of designing solar cell materials, using the electronic bandgap as the target property. We propose a new approach of using multifidelity data through leveraging machine learning models of both low- and high-fidelity data, where using predicted low-fidelity data as an input feature in the high-fidelity model can improve the impact of a multifidelity data approach. We show how tradeoffs of low- versus high-fidelity measurement cost and acquisition can impact the materials discovery process. We find that the use of multifidelity data has maximal impact on the materials discovery campaign when approximately five low-fidelity measurements per high-fidelity measurement are performed, and when the cost of low-fidelity measurements is approximately 5% or less than that of high-fidelity measurements. This work provides practical guidance and useful qualitative measures for improving materials discovery campaigns that involve multifidelity data.
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Narayan, Akil, Claude Gittelson, and Dongbin Xiu. "A Stochastic Collocation Algorithm with Multifidelity Models." SIAM Journal on Scientific Computing 36, no. 2 (January 2014): A495—A521. http://dx.doi.org/10.1137/130929461.

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Peng, Yijie, Jie Xu, Loo Hay Lee, Jianqiang Hu, and Chun-Hung Chen. "Efficient Simulation Sampling Allocation Using Multifidelity Models." IEEE Transactions on Automatic Control 64, no. 8 (August 2019): 3156–69. http://dx.doi.org/10.1109/tac.2018.2886165.

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Jasa, John, Pietro Bortolotti, Daniel Zalkind, and Garrett Barter. "Effectively using multifidelity optimization for wind turbine design." Wind Energy Science 7, no. 3 (May 11, 2022): 991–1006. http://dx.doi.org/10.5194/wes-7-991-2022.

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Abstract. Wind turbines are complex multidisciplinary systems that are challenging to design because of the tightly coupled interactions between different subsystems. Computational modeling attempts to resolve these couplings so we can efficiently explore new wind turbine systems early in the design process. Low-fidelity models are computationally efficient but make assumptions and simplifications that limit the accuracy of design studies, whereas high-fidelity models capture more of the actual physics but with increased computational cost. This paper details the use of multifidelity methods for optimizing wind turbine designs by using information from both low- and high-fidelity models to find an optimal solution at reduced cost. Specifically, a trust-region approach is used with a novel corrective function built from a nonlinear surrogate model. We find that for a diverse set of design problems – with examples given in rotor blade geometry design, wind turbine controller design, and wind power plant layout optimization – the multifidelity method finds the optimal design using 38 %–58 % of the computational cost of the high-fidelity-only optimization. The success of the multifidelity method in disparate applications suggests that it could be more broadly applied to other wind energy or otherwise generic applications.
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Rumpfkeil, Markus P., and Philip Beran. "Construction of Dynamic Multifidelity Locally Optimized Surrogate Models." AIAA Journal 55, no. 9 (September 2017): 3169–79. http://dx.doi.org/10.2514/1.j055834.

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Zhu, Xueyu, Akil Narayan, and Dongbin Xiu. "Computational Aspects of Stochastic Collocation with Multifidelity Models." SIAM/ASA Journal on Uncertainty Quantification 2, no. 1 (January 2014): 444–63. http://dx.doi.org/10.1137/130949154.

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Keshavarzzadeh, Vahid, Robert M. Kirby, and Akil Narayan. "Convergence Acceleration for Time-Dependent Parametric Multifidelity Models." SIAM Journal on Numerical Analysis 57, no. 3 (January 2019): 1344–68. http://dx.doi.org/10.1137/18m1170339.

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Howard, Amanda, Yucheng Fu, and Panos Stinis. "A multifidelity approach to continual learning for physical systems." Machine Learning: Science and Technology 5, no. 2 (May 16, 2024): 025042. http://dx.doi.org/10.1088/2632-2153/ad45b2.

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Abstract We introduce a novel continual learning method based on multifidelity deep neural networks. This method learns the correlation between the output of previously trained models and the desired output of the model on the current training dataset, limiting catastrophic forgetting. On its own the multifidelity continual learning method shows robust results that limit forgetting across several datasets. Additionally, we show that the multifidelity method can be combined with existing continual learning methods, including replay and memory aware synapses, to further limit catastrophic forgetting. The proposed continual learning method is especially suited for physical problems where the data satisfy the same physical laws on each domain, or for physics-informed neural networks, because in these cases we expect there to be a strong correlation between the output of the previous model and the model on the current training domain.
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Pienaar, Elsje. "Multifidelity Analysis for Predicting Rare Events in Stochastic Computational Models of Complex Biological Systems." Biomedical Engineering and Computational Biology 9 (January 2018): 117959721879025. http://dx.doi.org/10.1177/1179597218790253.

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Rare events such as genetic mutations or cell-cell interactions are important contributors to dynamics in complex biological systems, eg, in drug-resistant infections. Computational approaches can help analyze rare events that are difficult to study experimentally. However, analyzing the frequency and dynamics of rare events in computational models can also be challenging due to high computational resource demands, especially for high-fidelity stochastic computational models. To facilitate analysis of rare events in complex biological systems, we present a multifidelity analysis approach that uses medium-fidelity analysis (Monte Carlo simulations) and/or low-fidelity analysis (Markov chain models) to analyze high-fidelity stochastic model results. Medium-fidelity analysis can produce large numbers of possible rare event trajectories for a single high-fidelity model simulation. This allows prediction of both rare event dynamics and probability distributions at much lower frequencies than high-fidelity models. Low-fidelity analysis can calculate probability distributions for rare events over time for any frequency by updating the probabilities of the rare event state space after each discrete event of the high-fidelity model. To validate the approach, we apply multifidelity analysis to a high-fidelity model of tuberculosis disease. We validate the method against high-fidelity model results and illustrate the application of multifidelity analysis in predicting rare event trajectories, performing sensitivity analyses and extrapolating predictions to very low frequencies in complex systems. We believe that our approach will complement ongoing efforts to enable accurate prediction of rare event dynamics in high-fidelity computational models.
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Journal articles

Dissertations / Theses on the topic "Multifidelity models":

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Robinson, Theresa Dawn 1978. "Surrogate-based optimization using multifidelity models with variable parameterization." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/39666.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2007.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 131-138).
Engineers are increasingly using high-fidelity models for numerical optimization. However, the computational cost of these models, combined with the large number of objective function and constraint evaluations required by optimization methods, can render such optimization computationally intractable. Surrogate-based optimization (SBO) - optimization using a lower-fidelity model most of the time, with occasional recourse to the high-fidelity model - is a proven method for reducing the cost of optimization. One branch of SBO uses lower-fidelity physics models of the same system as the surrogate. Until now however, surrogates using a different set of design variables from that of the high-fidelity model have not been available to use in a provably convergent numerical optimization. New methods are herein developed and demonstrated to reduce the computational cost of numerical optimization of variableparameterization problems, that is, problems for which the low-fidelity model uses a different set of design variables from the high-fidelity model.
(cont.) Four methods are presented to perform mapping between variable-parameterization spaces, the last three of which are new: space mapping, corrected space mapping, a mapping based on proper orthogonal decomposition (POD), and a hybrid between POD mapping and space mapping. These mapping methods provide links between different models of the same system and have further applications beyond formal optimization strategies. On an unconstrained airfoil design problem, it achieved up to 40% savings in highfidelity function evaluations. On a constrained wing design problem it achieved 76% time savings, and on a bat flight design problem, it achieved 45% time savings. On a large-scale practical aerospace application, such time savings could represent weeks.
by Theresa D. Robinson.
Ph.D.
2

Chandrasekhar, Ashok. "Interfacing geometric design models to analyzable product models with multifidelity and mismatched analysis geometry." Thesis, Georgia Institute of Technology, 1999. http://hdl.handle.net/1853/17769.

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Battisti, Beatrice. "Modélisation multi-échelle et multi-fidélité pour des extracteurs d'énergie marine." Electronic Thesis or Diss., Bordeaux, 2024. http://www.theses.fr/2024BORD0072.

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Le secteur maritime s’oriente de plus en plus vers les convertisseurs d’énergie des vagues (WECs), en particulier vers des fermes de WECs. Cependant, les simulations numériques sont complexes, et bien que les modèles haute fidélité assurent la précision, leurs exigences computationnelles ont stimulé l’intérêt pour les techniques de réduction de modèles. Les modèles à ordre réduit basés sur la Décomposition Orthogonale aux valeurs Propres (POD) sont efficaces dans les écoulements monophasiques, mais rencontrent des problèmes de stabilité avec les écoulements multiphasiques. Un modèle multifidélité intègre la CFD (Computational Fluid Dynamics) pour le champ proche des WECs et la POD pour la propagation des vagues en champ lointain. L’échange d’informations assure une description précise de l’écoulement et de la dynamique des flotteurs. Les tests confirment son efficacité, réduisant significativement la charge computationnelle, cruciale pour aborder l’optimisation des fermes de WECs
The marine sector is increasingly turning to wave energy converters (WECs) for clean energy generation. For commercial-scale production, WEC farm deployment is essential, but requires complex numerical simulations. While high-fidelity models like Computational Fluid Dynamics (CFD) ensure accuracy, their substantial computational demands have prompt interest in model order reduction techniques. Proper Orthogonal Decomposition (POD) projection-based reduced order models have proven effective in monophase flows, yet face stability issues with multiphase flows. A proposed multi-fidelity model integrates CFD for WEC near-field description, and POD for far-field wave propagation. Bidirectional information exchange ensures precise flow reconstruction and floater dynamics description. Testing confirms its efficacy in various scenarios, significantly reducing the computational burden, decisive for tackling WEC farm design and optimization
Il settore marittimo è sempre più orientato verso i convertitori di energia delle onde (WECs), in particolare verso i parchi di WECs. Tuttavia, le simulazioni numeriche sono complesse e, sebbene i modelli ad alta fedeltà assicurino precisione, i loro requisiti computazionali hanno stimolato l’interesse per le tecniche di riduzione di modello. I modelli a ordine ridotto basati sulla Decomposizione Ortogonale ai valori Propri (POD) sono efficaci per flussi monofase, ma incontrano problemi di stabilità con i flussi multifase. Un modello multifideltà è proposto, che integra la CFD (Computational Fluid Dynamics) per il campo vicino ai WECs e la POD per la propagazione delle onde nel campo lontano. Lo scambio di informazioni assicura una descrizione precisa del flusso e della dinamica dei WECs. I test ne confermano l’efficacia, riducendo significativamente il carico computazionale, cruciale per affrontare l’ottimizzazione dei parchi di WECs
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Fossà, Alberto. "Propagation multi-fidélité d’incertitude orbitale en présence d’accélérations stochastiques." Electronic Thesis or Diss., Toulouse, ISAE, 2024. http://www.theses.fr/2024ESAE0009.

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Le problème de la propagation non linéaire d’incertitude est crucial en astrodynamique, car tous les systèmes d’intérêt pratique, allant de la navigation à la détermination d’orbite et au suivi de cibles, impliquent des non-linéarités dans leurs modèles dynamiques et de mesure. Un sujet d’intérêt est la propagation précise d’incertitude à travers la dynamique orbitale non linéaire, une exigence fondamentale dans plusieurs applications telles que la surveillance de l’espace, la gestion du trafic spatial et la fin de vie des satellites. Étant donnée une représentation dimensionnelle finie de la fonction de densité de probabilité (pdf) de l’état initial, l’objectif est d’obtenir une représentation similaire de cette pdf à tout moment futur. Ce problème a été historiquement abordé avec des méthodes linéarisées ou des simulations de Monte Carlo (MC), toutes deux inadaptées pour satisfaire la demande d’un nombre croissant d’applications. Les méthodes linéarisées sont très performantes, mais ne peuvent pas gérer de fortes non-linéarités ou de longues fenêtres de propagation en raison de la validité locale de la linéarisation. En revanche, les méthodes MC peuvent gérer tout type de non-linéarité, mais sont trop coûteuses en termes de calcul pour toute tâche nécessitant la propagation de plusieurs pdf. Au lieu de cela, cette thèse exploite des méthodes multi-fidélité et des techniques d’algèbre différentielle (DA) pour développer des méthodes efficaces pour la propagation précise des incertitudes à travers des systèmes dynamiques non linéaires. La première méthode, appelée low-order automatic domain splitting (LOADS), représente l’incertitude avec un ensemble de polynômes de Taylor du deuxième ordre et exploite une mesure de non-linéarité basée sur la DA pour ajuster leur nombre en fonction de la dynamique locale et de la précision requise. Un modèle adaptatif de mélange Gaussien (GMM) est ensuite développé en associant chaque polynôme à un noyau pondéré pour obtenir une représentation analytique de la pdf d’état. En outre, une méthode multi-fidélité est proposée pour réduire le coût computationnel des algorithmes précédents tout en conservant une précision similaire. La méthode GMM est dans ce cas exécutée sur un modèle dynamique à faible fidélité, et seules les moyennes des noyaux sont propagées ponctuellement dans une dynamique à haute fidélité pour corriger la pdf à faible fidélité. Si les méthodes précédentes traitent de la propagation d’une incertitude initiale dans un modèle dynamique déterministe, les effets des forces mal ou non modélisées sont enfin pris en compte pour améliorer le réalisme des statistiques propagées. Dans ce cas, la méthode multi-fidélité est d’abord utilisée pour propager l’incertitude initiale dans un modèle dynamique déterministe de faible fidélité. Les propagations ponctuelles sont ensuite remplacées par une propagation polynomiale des moments de la pdf dans un système dynamique stochastique. Ces moments modélisent les effets des accélérations stochastiques sur les moyennes des noyaux, et couplés à la méthode GMM, ils fournissent une description de la pdf qui tient compte de l’incertitude initiale et des effets des forces négligées. Les méthodes proposées sont appliquées au problème de la propagation d’incertitude en orbite, et leurs performances sont évaluées dans différents régimes orbitaux. Les résultats démontrent leur efficacité pour une propagation précise de l’incertitude initiale et des effets du bruit du processus à une fraction du coût de calcul des simulations MC. La méthode LOADS est ensuite utilisée pour résoudre le problème de la détermination initiale d’orbite en exploitant les informations sur l’incertitude des mesures, et pour développer une méthode de prétraitement des données qui améliore la robustesse des algorithmes de détermination d’orbite. Ces outils sont enfin validés sur des observations réelles d’un objet en orbite de transfert géostationnaire
The problem of nonlinear uncertainty propagation (UP) is crucial in astrodynamics since all systems of practical interest, ranging from navigation to orbit determination (OD) and target tracking, involve nonlinearities in their dynamics and measurement models. One topic of interest is the accurate propagation of uncertainty through the nonlinear orbital dynamics, a fundamental requirement in several applications such as space surveillance and tracking (SST), space traffic management (STM), and end-of-life (EOL) disposal. Given a finite-dimensional representation of the probability density function (pdf) of the initial state, the main goal is to obtain a similar representation of the state pdf at any future time. This problem has been historically tackled with either linearized methods or Monte Carlo (MC) simulations, both of which are unsuitable to satisfy the demand of a rapidly growing number of applications. Linearized methods are light on computational resources, but cannot handle strong nonlinearities or long propagation windows due to the local validity of the linearization. In contrast, MC methods can handle any kind of nonlinearity, but are too computationally expensive for any task that requires the propagation of several pdfs. Instead, this thesis leverages multifidelity methods and differential algebra (DA) techniques to develop computationally efficient methods for the accurate propagation of uncertainties through nonlinear dynamical systems. The first method, named low-order automatic domain splitting (LOADS), represents the uncertainty with a set of second-order Taylor polynomials and leverages a DA-based measure of nonlinearity to adjust their number based on the local dynamics and the required accuracy. An adaptive Gaussian mixture model (GMM) method is then developed by associating each polynomial to a weighted Gaussian kernel, thus obtaining an analytical representation of the state pdf. Going further, a multifidelity method is proposed to reduce the computational cost of the former algorithms while retaining a similar accuracy. The adaptive GMM method is in this case run on a low-fidelity dynamical model, and only the expected values of the kernels are propagated point-wise in high-fidelity dynamics to compute a posteriori correction of the low-fidelity state pdf. If the former methods deal with the propagation of an initial uncertainty through a deterministic dynamical model, the effects of mismodeled or unmodeled forces are finally considered to further enhance the realism of the propagated statistics. In this case, the multifidelity GMM method is used at first to propagate the initial uncertainty through a low-fidelity, deterministic dynamical model. The point-wise propagations are then replaced with a DA-based algorithm to efficiently propagate a polynomial representation of the moments of the pdf in a stochastic dynamical system. These moments model the effects of stochastic accelerations on the deterministic kernels’ means, and coupled with the former GMM provide a description of the propagated state pdf that accounts for both the uncertainty in the initial state and the effects of neglected forces. The proposed methods are applied to the problem of orbit UP, and their performance is assessed in different orbital regimes. The results demonstrate the effectiveness of these methods in accurately propagating the initial uncertainty and the effects of process noise at a fraction of the computational cost of high-fidelity MC simulations. The LOADS method is then employed to solve the initial orbit determination (IOD) problem by exploiting the information on measurement uncertainty and to develop a preprocessing scheme aimed at improving the robustness of batch OD algorithms. These tools are finally validated on a set of real observations for an object in geostationary transfer orbit (GTO)
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Bryson, Dean Edward. "A Unified, Multifidelity Quasi-Newton Optimization Method with Application to Aero-Structural Design." University of Dayton / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1510146591195367.

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De, lozzo Matthias. "Modèles de substitution spatio-temporels et multifidélité : Application à l'ingénierie thermique." Thesis, Toulouse, INSA, 2013. http://www.theses.fr/2013ISAT0027/document.

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Cette thèse porte sur la construction de modèles de substitution en régimes transitoire et permanent pour la simulation thermique, en présence de peu d'observations et de plusieurs sorties.Nous proposons dans un premier temps une construction robuste de perceptron multicouche bouclé afin d'approcher une dynamique spatio-temporelle. Ce modèle de substitution s'obtient par une moyennisation de réseaux de neurones issus d'une procédure de validation croisée, dont le partitionnement des observations associé permet d'ajuster les paramètres de chacun de ces modèles sur une base de test sans perte d'information. De plus, la construction d'un tel perceptron bouclé peut être distribuée selon ses sorties. Cette construction est appliquée à la modélisation de l'évolution temporelle de la température en différents points d'une armoire aéronautique.Nous proposons dans un deuxième temps une agrégation de modèles par processus gaussien dans un cadre multifidélité où nous disposons d'un modèle d'observation haute-fidélité complété par plusieurs modèles d'observation de fidélités moindres et non comparables. Une attention particulière est portée sur la spécification des tendances et coefficients d'ajustement présents dans ces modèles. Les différents krigeages et co-krigeages sont assemblés selon une partition ou un mélange pondéré en se basant sur une mesure de robustesse aux points du plan d'expériences les plus fiables. Cette approche est employée pour modéliser la température en différents points de l'armoire en régime permanent.Nous proposons dans un dernier temps un critère pénalisé pour le problème de la régression hétéroscédastique. Cet outil est développé dans le cadre des estimateurs par projection et appliqué au cas particulier des ondelettes de Haar. Nous accompagnons ces résultats théoriques de résultats numériques pour un problème tenant compte de différentes spécifications du bruit et de possibles dépendances dans les observations
This PhD thesis deals with the construction of surrogate models in transient and steady states in the context of thermal simulation, with a few observations and many outputs.First, we design a robust construction of recurrent multilayer perceptron so as to approach a spatio-temporal dynamic. We use an average of neural networks resulting from a cross-validation procedure, whose associated data splitting allows to adjust the parameters of these models thanks to a test set without any information loss. Moreover, the construction of this perceptron can be distributed according to its outputs. This construction is applied to the modelling of the temporal evolution of the temperature at different points of an aeronautical equipment.Then, we proposed a mixture of Gaussian process models in a multifidelity framework where we have a high-fidelity observation model completed by many observation models with lower and no comparable fidelities. A particular attention is paid to the specifications of trends and adjustement coefficients present in these models. Different kriging and co-krigings models are put together according to a partition or a weighted aggregation based on a robustness measure associated to the most reliable design points. This approach is used in order to model the temperature at different points of the equipment in steady state.Finally, we propose a penalized criterion for the problem of heteroscedastic regression. This tool is build in the case of projection estimators and applied with the Haar wavelet. We also give some numerical results for different noise specifications and possible dependencies in the observations
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(7033289), Viraj Dipakbhai Gandhi. "PARAMETRIC DESIGNS AND WEIGHT OPTIMIZATION USING DIRECT AND INDIRECT AERO-STRUCTURE LOAD TRANSFER METHODS." Thesis, 2019.

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Within the aerospace design, analysis and optimization community, there is an increasing demand to finalize the preliminary design phase of the wing as quickly as possible without losing much on accuracy. This includes rapid generation of designs, an early adaption of higher fidelity models and automation in structural analysis of the internal structure of the wing. To perform the structural analysis, the aerodynamic load can be transferred to the wing using many different methods. Generally, for preliminary analysis, indirect load transfer method is used and for detailed analysis, direct load transfer method is used. For the indirect load transfer method, load is discretized using shear-moment-torque (SMT) curve and applied to ribs of the wing. For the direct load transfer method, the load is distributed using one-way Fluid-Structure Interaction (FSI) and applied to the skin of the wing. In this research, structural analysis is performed using both methods and the nodal displacement is compared. Further, to optimize the internal structure, iterative changes are made in the number of structural members. To accommodate these changes in geometry as quickly as possible, the parametric design method is used through Engineering SketchPad (ESP). ESP can also provide attributions the geometric feature and generate multi-fidelity models consistently. ESP can generate the Nastran mesh file (.bdf) with the nodes and the elements grouped according to their geometric attributes. In this research, utilizing the attributions and consistency in multi-fidelity models an API is created between ESP and Nastran to automatize the multi-fidelity structural optimization. This API generates the design with appropriate parameters and mesh file using ESP. Through the attribution in the mesh file, the API works as a pre-processor to apply material properties, boundary condition, and optimization parameters. The API sends the mesh file to Nastran and reads the results file to iterate the number of the structural member in design. The result file is also used to transfer the nodal deformation from lower-order fidelity structural models onto the higher-order ones to have multi-fidelity optimization. Here, static structural optimization on the whole wing serves as lower fidelity model and buckling optimization on each stiffened panel serves as higher fidelity model. To further extend this idea, a parametric model of the whole aircraft is also created.

Book chapters on the topic "Multifidelity models":

1

Van Buren, Kendra, and François Hemez. "Robust-Optimal Design Using Multifidelity Models." In Model Validation and Uncertainty Quantification, Volume 3, 199–205. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15224-0_21.

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Eldred, Michael S., Leo W. T. Ng, Matthew F. Barone, and Stefan P. Domino. "Multifidelity Uncertainty Quantification Using Spectral Stochastic Discrepancy Models." In Handbook of Uncertainty Quantification, 991–1036. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-12385-1_25.

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Eldred, Michael S., Leo W. T. Ng, Matthew F. Barone, and Stefan P. Domino. "Multifidelity Uncertainty Quantification Using Spectral Stochastic Discrepancy Models." In Handbook of Uncertainty Quantification, 1–45. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-11259-6_25-1.

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Conference papers on the topic "Multifidelity models":

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Reuter, Bryan, Gianluca Geraci, Timothy Wildey, and Michael Eldred. "Multifidelity Uncertainty Quantification For Non-Deterministic Models." In Proposed for presentation at the ECCOMAS Congress 2022 held June 5-9, 2022 in Oslo, Norway. US DOE, 2022. http://dx.doi.org/10.2172/2003426.

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Rezaeiravesh, S., R. Vinuesa, and P. Schlatter. "Towards Multifidelity Models with Calibration for Turbulent Flows." In 14th WCCM-ECCOMAS Congress. CIMNE, 2021. http://dx.doi.org/10.23967/wccm-eccomas.2020.348.

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Cocco, Alessandro, and Alberto Savino. "Tiltrotor Whirl-Flutter Assessment by Multifidelity Aerodynamic Models." In AIAA SCITECH 2024 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2024. http://dx.doi.org/10.2514/6.2024-1850.

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Chell, Brian, Steven Hoffenson, and Mark R. Blackburn. "Comparing Multifidelity Model Management Strategies for Multidisciplinary Design Optimization." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97859.

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Abstract:
Abstract Multifidelity optimization leverages the fast run times of low-fidelity models with the accuracy of high-fidelity models, in order to conserve computing resources while still reaching optimal solutions. This work focuses on the multidisciplinary multifidelity optimization of an unmanned aerial system model with finite element analysis and computational fluid dynamics simulations in-the-loop. A two-step process is used where the lower-fidelity models are optimized, and then the optimizer is used as a starting point for the higher-fidelity models. By starting the high-fidelity optimization routine at a nearly optimal section of the design space, the computing resources required for optimization are expected to decrease when using gradient-based algorithms. Results show that, at least in some cases, the multifidelity workflows save time over optimizing the original high fidelity model alone. However, the model management strategy did not find statistically significant differences between the differing optimization approaches when used on this test problem.
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Robinson, Theresa, Michael Eldred, Karen Willcox, and Robert Haimes. "Strategies for Multifidelity Optimization with Variable Dimensional Hierarchical Models." In 47th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference
14th AIAA/ASME/AHS Adaptive Structures Conference
7th. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-1819.

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Reuter, Bryan, Gianluca Geraci, and Timothy Wildey. "Efficient Multifidelity Strategies for Uncertainty Quantification of Non-Deterministic Models." In Proposed for presentation at the SIAM UQ 2022 held April 12-15, 2022 in Atlanta, GA US. US DOE, 2022. http://dx.doi.org/10.2172/2002277.

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Karali, Hasan, Gokhan Inalhan, and Antonios Tsourdos. "AI-Based Multifidelity Surrogate Models to Develop Next Generation Modular UCAVs." In AIAA SCITECH 2023 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2023. http://dx.doi.org/10.2514/6.2023-0670.

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Li, Wu, and Karl Geiselhart. "Multiobjective Multidisciplinary Optimization of Low-Boom Supersonic Transports Using Multifidelity Models." In AIAA SCITECH 2022 Forum. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2022. http://dx.doi.org/10.2514/6.2022-2097.

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Guo, Zhendong, Wei Sun, Liming Song, Jun Li, and Zhenping Feng. "Generative Transfer Optimization for Aerodynamic Design." In GPPS Xi'an21. GPPS, 2022. http://dx.doi.org/10.33737/gpps21-tc-225.

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Abstract:
Transfer optimization, one type of optimization methods, which leverages knowledge of the completed tasks to accelerate the design progress of a new task, has been in widespread use in machine learning community. However, when applying transfer optimization to accelerate the progress of aerodynamic shape optimization (ASO), two challenges are encountered in sequence, that is, (1) how to build a shared design space among the related aerodynamic design tasks, and (2) how to exchange information between tasks most efficiently. To address the first challenge, a datadriven generative model is used to learn airfoil representations from the existing database, with the aim of synthesizing various airfoil shapes in a shared design space. To address the second challenge, both singleand multifidelity Gaussian processes (GPs) are employed to carry out optimization. On one hand, the multifidelity GP is used to leverage knowledge from the completed tasks. On the other hand, mutual learning is established between singleand multifidelity GP models by exchanging information between them in each optimization cycle. With the above, a generative transfer optimization (GTO) framework is proposed to shorten the design cycle of aerodynamic design. Through airfoil optimizations at different working conditions, the effectiveness of the proposed GTO framework is demonstrated.
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Eldred, Michael, and Daniel Dunlavy. "Formulations for Surrogate-Based Optimization with Data Fit, Multifidelity, and Reduced-Order Models." In 11th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference. Reston, Virigina: American Institute of Aeronautics and Astronautics, 2006. http://dx.doi.org/10.2514/6.2006-7117.

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Reports on the topic "Multifidelity models":

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Blonigan, Patrick Joseph, Gianluca Geraci, Francesco Rizzi, Michael S. Eldred, and Kevin Carlberg. On-line Generation and Error Handling for Surrogate Models within Multifidelity Uncertainty Quantification. Office of Scientific and Technical Information (OSTI), September 2019. http://dx.doi.org/10.2172/1567834.

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Turinsky, Paul. Development of Adaptive Model Refinement (AMoR) for Multiphysics and Multifidelity Problems. Office of Scientific and Technical Information (OSTI), February 2015. http://dx.doi.org/10.2172/1169938.

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Hough, Patricia Diane, Genetha Anne Gray, Joseph Pete Jr Castro, .), and Anthony Andrew Giunta. Developing a computationally efficient dynamic multilevel hybrid optimization scheme using multifidelity model interactions. Office of Scientific and Technical Information (OSTI), January 2006. http://dx.doi.org/10.2172/877137.

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