Relevant bibliographies by topics / Adaptive parametric sampling

Academic literature on the topic 'Adaptive parametric sampling'

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Published: 29 August 2023

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Journal articles on the topic "Adaptive parametric sampling"

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Rafiq, Danish, and Mohammad Abid Bazaz. "Adaptive parametric sampling scheme for nonlinear model order reduction." Nonlinear Dynamics 107, no. 1 (November 2, 2021): 813–28. http://dx.doi.org/10.1007/s11071-021-07025-7.

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Azencott, R., A. Beri, and I. Timofeyev. "Adaptive Sub-sampling for Parametric Estimation of Gaussian Diffusions." Journal of Statistical Physics 139, no. 6 (May 1, 2010): 1066–89. http://dx.doi.org/10.1007/s10955-010-9975-y.

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Borggaard, Jeff, Kevin R. Pond, and Lizette Zietsman. "Parametric Reduced Order Models Using Adaptive Sampling and Interpolation." IFAC Proceedings Volumes 47, no. 3 (2014): 7773–78. http://dx.doi.org/10.3182/20140824-6-za-1003.02664.

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Liu, Ying, Hongguang Li, Huanyu Du, Ningke Tong, and Guang Meng. "An adaptive sampling procedure for parametric model order reduction by matrix interpolation." Journal of Low Frequency Noise, Vibration and Active Control 39, no. 4 (June 15, 2019): 821–34. http://dx.doi.org/10.1177/1461348419851595.

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An adaptive sampling approach for parametric model order reduction by matrix interpolation is developed. This approach is based on an efficient exploration of the candidate parameter sets and identification of the points with maximum errors. An error indicator is defined and used for fast evaluation of the parameter points in the configuration space. Furthermore, the exact error of the model with maximum error indicator is calculated to determine whether the adaptive sampling procedure reaches a desired error tolerance. To improve the accuracy, the orthogonal eigenvectors are utilized as the reduced-order basis. The proposed adaptive sampling procedure is then illustrated by application in the moving coil of electrical-dynamic shaker. It is shown that the new method can sample the parameter space adaptively and efficiently with the assurance of the resulting reduced-order models’ accuracy.
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Chen, Yi-Wen, and Wen-Hsiao Peng. "Parametric OBMC for Pixel-Adaptive Temporal Prediction on Irregular Motion Sampling Grids." IEEE Transactions on Circuits and Systems for Video Technology 22, no. 1 (January 2012): 113–27. http://dx.doi.org/10.1109/tcsvt.2011.2158341.

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Woudt, Edwin, Pieter-Tjerk de Boer, and Jan-Kees van Ommeren. "Improving Adaptive Importance Sampling Simulation of Markovian Queueing Models using Non-parametric Smoothing." SIMULATION 83, no. 12 (December 2007): 811–20. http://dx.doi.org/10.1177/0037549707087223.

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Jia, Gaofeng, and Alexandros A. Taflanidis. "Non-parametric stochastic subset optimization utilizing multivariate boundary kernels and adaptive stochastic sampling." Advances in Engineering Software 89 (November 2015): 3–16. http://dx.doi.org/10.1016/j.advengsoft.2015.06.014.

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Lu, Kuan, Haopeng Zhang, Kangyu Zhang, Yulin Jin, Shibo Zhao, Chao Fu, and Yushu Chen. "The Transient POD Method Based on Minimum Error of Bifurcation Parameter." Mathematics 9, no. 4 (February 16, 2021): 392. http://dx.doi.org/10.3390/math9040392.

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An invariable order reduction model cannot be obtained by the adaptive proper orthogonal decomposition (POD) method in parametric domain, there exists uniqueness of the model with different conditions. In this paper, the transient POD method based on the minimum error of bifurcation parameter is proposed and the order reduction conditions in the parametric domain are provided. The order reduction model equivalence of optimal sampling length is discussed. The POD method was applied for order reduction of a high-dimensional rotor system supported by sliding bearings in a certain speed range. The effects of speed, initial conditions, sampling length, and mode number on parametric domain order reduction are discussed. The existence of sampling length was verified, and two- and three-degrees-of-freedom (DOF) invariable order reduction models were obtained by proper orthogonal modes (POM) on the basis of optimal sampling length.
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Ourbih-Tari, Megdouda, and Mahdia Azzal. "Survival function estimation with non parametric adaptive refined descriptive sampling algorithm: A case study." Communications in Statistics - Theory and Methods 46, no. 12 (April 25, 2016): 5840–50. http://dx.doi.org/10.1080/03610926.2015.1065328.

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Morio, Jérôme. "Non-parametric adaptive importance sampling for the probability estimation of a launcher impact position." Reliability Engineering & System Safety 96, no. 1 (January 2011): 178–83. http://dx.doi.org/10.1016/j.ress.2010.08.006.

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Dissertations / Theses on the topic "Adaptive parametric sampling"

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Chetry, Manisha. "Advanced reduced-order modeling and parametric sampling for non-Newtonian fluid flows." Electronic Thesis or Diss., Ecole centrale de Nantes, 2023. http://www.theses.fr/2023ECDN0011.

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Le sujet de cette thèse porte sur laréduction d'ordre de modèle (MOR) deproblèmes d'écoulement non-Newtonianparamétrés qui ont des applicationsindustrielles importantes. Les méthodestraditionnelles de réduction de l'ordre desmodèles limitent les performances decalcul de ces problèmes hautement nonlinéaires, nous suggérons donc une techniqued'hyper-réduction avancée basée sur uneapproximation sparse de l'évaluation destermes non linéaire à complexité reduite.Nous proposons également une stratégie destabilisation hors ligne pour stabiliser le modèleconstitutif dans le modèle d'ordre réduit quiest moins cher à calculer tout en maintenant laprécision du modèle d'ordre complet. Lacombinaison des deux réduit drastiquement lecoût du processeur, augmentantinévitablement les performances du MOR. Cetravail est validé sur deux problèmes debenchmark. En outre, une stratégied'échantillonnage adaptatif est égalementprésentée dans ce manuscrit, qui est réaliséeen tirant parti de l'approximation des modèlesmulti-fidélité. Vers la fin de la thèse, nousabordons un autre problème qui estgénéralement observé dans les cas où desmaillages d'éléments finis adaptatifs sontdéployés. Dans de tels cas, les méthodes MORne parviennent pas à produire unereprésentation de faible dimension car lessnapshots ne sont pas des vecteurs de mêmelongueur. Par conséquent, nous suggérons uneméthodologie qui peut générer des fonctionsde base réduites pour des snapshotsadaptative
The subject of this thesis concernsmodel-order reduction (MOR) of parameterizednon-Newtonian flow problems that havesignificant industrial applications. TraditionalMOR methods constrain the computationalperformance of such highly nonlinear problems,so we suggest a state-of-the-art hyper-reductiontechnique based on a sparse approximation totackle the evaluation of nonlinear terms at muchreduced complexity. We also provide offlinestabilization strategy for stabilizing theconstitutive model in the reduced order modelframework that is less expensive to computewhile maintaining the full order model's (FOM)accuracy. Combining the two significantlylowers the CPU cost as compared to the FOMevaluation which inevitably boosts MORperformance. This work is validated on twobenchmark flow problems. Additionally, anadaptive sampling strategy is also presented inthis manuscript which is achieved byleveraging multi-fidelity model approximation.Towards the end of the thesis, we addressanother issue that is typically observed forcases when adaptive finite element meshesare deployed. In such cases, MOR methods failto produce a low-dimensional representationsince the snapshots are not vectors of samelength. We therefore, suggest an alternatemethod that can generate reduced basisfunctions for database of space-adaptedsnapshots
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Castro, Rui M. "Active learning and adaptive sampling for non-parametric inference." Thesis, 2008. http://hdl.handle.net/1911/22265.

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This thesis presents a general discussion of active learning and adaptive sampling. In many practical scenarios it is possible to use information gleaned from previous observations to focus the sampling process, in the spirit of the "twenty-questions" game. As more samples are collected one can learn how to improve the sampling process by deciding where to sample next, for example. These sampling feedback techniques are generically known as active learning or adaptive sampling. Although appealing, analysis of such methodologies is difficult, since there are strong dependencies between the observed data. This is especially important in the presence of measurement uncertainty or noise. The main thrust of this thesis is to characterize the potential and fundamental limitations of active learning, particularly in non-parametric settings. First, we consider the probabilistic classification setting. Using minimax analysis techniques we investigate the achievable rates of classification error convergence for broad classes of distributions characterized by decision boundary regularity and noise conditions (which describe the observation noise near the decision boundary). The results clearly indicate the conditions under which one can expect significant gains through active learning. Furthermore we show that the learning rates derived are tight for "boundary fragment" classes in d-dimensional feature spaces when the feature marginal density is bounded from above and below. Second we study the problem of estimating an unknown function from noisy point-wise samples, where the sample locations are adaptively chosen based on previous samples and observations, as described above. We present results characterizing the potential and fundamental limits of active learning for certain classes of nonparametric regression problems, and also present practical algorithms capable of exploiting the sampling adaptivity and provably improving upon non-adaptive techniques. Our active sampling procedure is based on a novel coarse-to-fine strategy, based on and motivated by the success of spatially-adaptive methods such as wavelet analysis in nonparametric function estimation. Using the ideas developed when solving the function regression problem we present a greedy algorithm for estimating piecewise constant functions with smooth boundaries that is near minimax optimal but is computationally much more efficient than the best dictionary based method (in this case wedgelet approximations). Finally we compare adaptive sampling (where feedback guiding the sampling process is present) with non-adaptive compressive sampling (where non-traditional projection samples are used). It is shown that under mild noise compressive sampling can be competitive with adaptive sampling, but adaptive sampling significantly outperforms compressive sampling in lower signal-to-noise conditions. Furthermore this work also helps the understanding of the different behavior of compressive sampling under noisy and noiseless settings.
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Book chapters on the topic "Adaptive parametric sampling"

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Quinn, J. A., F. C. Langbein, R. R. Martin, and G. Elber. "Density-Controlled Sampling of Parametric Surfaces Using Adaptive Space-Filling Curves." In Geometric Modeling and Processing - GMP 2006, 465–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11802914_33.

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Figueiredo, Luiz Henrique de. "Adaptive Sampling of Parametric Curves." In Graphics Gems V, 173–78. Elsevier, 1995. http://dx.doi.org/10.1016/b978-0-12-543457-7.50032-2.

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Conference papers on the topic "Adaptive parametric sampling"

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Varona, Maria Cruz, Mashuq-un-Nabiz, and Boris Lohmann. "Automatic adaptive sampling in parametric Model Order Reduction by Matrix Interpolation." In 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, 2017. http://dx.doi.org/10.1109/aim.2017.8014062.

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Hsu, Charles, and Harold Szu. "Low-discrepancy sampling of parametric surface using adaptive space-filling curves (SFC)." In SPIE Sensing Technology + Applications, edited by Harold H. Szu and Liyi Dai. SPIE, 2014. http://dx.doi.org/10.1117/12.2053306.

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Hombal, Vadiraj, Arthur Sanderson, and Richard Blidberg. "A Non-Parametric Iterative Algorithm For Adaptive Sampling And Robotic Vehicle Path Planning." In 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2006. http://dx.doi.org/10.1109/iros.2006.282561.

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Ji, Runda, and Qiqi Wang. "Aerodynamic Risk Assessment using Parametric, Three-Dimensional Unstructured, High-Fidelity CFD and Adaptive Sampling." In AIAA AVIATION 2021 FORUM. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2021. http://dx.doi.org/10.2514/6.2021-2461.

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Ma, Jian-Wei, De-Ning Song, Zhen-Yuan Jia, Ning Zhang, Guo-Qing Hu, and Wei-Wei Su. "Adaptive Pre-Compensation of the Contouring Error for High-Precision Parametric Curved Contour Following." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-71284.

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Tasks of parametric curved contour following can be widely seen in computer-numerical-control (CNC) machining of parts with complex geometric features. Due to the existence of the contouring error in contour-following tasks, the machining precision of CNC machine tools will be seriously degraded. To reduce this error, methods such as cross-coupled control are extensively researched. However, these methods focus on compensation of the already happened contouring error, based on approximation of the error value according to the online measured actual motion positions. This paper presents an adaptive real-time pre-compensation approach, so as to control the contouring error before it really occurs. First, actual motion positions of the feed axes at the next sampling period are predicted, according to the z-domain model of each feed-drive system. To improve the adaptive capacity of the actual position prediction, the feed-drive models are identified online using the least-square method. After that, an accurate contouring-error calculation method, based on tangential-error backstepping using a moving frame, is proposed. Finally, the adaptive estimated contouring error at the next sampling period is compensated at the current period, thus beforehand improving the contour accuracy. Simulation and experimental tests are conducted to demonstrate the feasibility of the presented methods. From the testing results, it can be seen that the presented error-estimation method can precisely compute the contouring error, and the pre-compensation approach improves the contour-tracking accuracy dramatically, which is of great significance for improving the machining precision of the CNC machine tools.
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Weaver-Rosen, Jonathan M., and Richard J. Malak. "Efficient Parametric Optimization for Expensive Single Objective Problems." 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/detc2020-22113.

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Abstract Parametric optimization solves optimization problems as a function of uncontrollable or unknown parameters. Such an approach allows an engineer to gather more information than traditional optimization procedures during design. Existing methods for parametric optimization of computationally or monetarily expensive functions can be too time-consuming or impractical to solve. Therefore, new methods for the parametric optimization of expensive functions need to be explored. This work proposes a novel algorithm that leverages the advantages of two existing optimization algorithms. This new algorithm is called the efficient parametric optimization (EPO) algorithm. EPO enables adaptive sampling of a high-fidelity design space using an inexpensive low-fidelity response surface model. Such an approach largely reduces the required number of expensive high-fidelity computations. The proposed method is benchmarked using analytic test problems and used to evaluate a case study requiring finite element analysis. Results show that EPO performs as well as or better than the existing alternative, P3GA, for these problems given an allowable number of function evaluations.
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Alinejad, F., D. Botto, M. Gola, and A. Bessone. "Reduction of the Design Space to Optimize Blade Fir-Tree Attachments." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-75781.

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The blade attachment, both dovetail or fir-tree, transfers the centrifugal load from the blade to the disc, generating high mean and peak stresses in notches as well as on contact surfaces. Hence, the strength of the attachment is one of the main concern of the designers for improving the performance of the engine and several optimization procedure have been put forward to minimize the state of stress in the attachment for a given centrifugal load. The optimization process is generally driven by a parametric model. The selection of the proper parameters and their variation ranges represent one of the main issues for the process to converge in a reasonable amount of time. Simulation methods and optimization algorithms have been improved a lot in the past years. Nevertheless, the computational effort of the finite element analysis involved in the optimization procedure of complex geometries remains a critical task. Moreover, an accurate evaluation of the local contact stresses is highly dependent on the mesh refinement, increasing the computing time of the whole optimization process. Moreover, a multi-objective optimization, in addition to robustness design approach, is the designer tool to improve the attachment performance. The searching domain reduction of the optimization process improves the computational performance reducing the convergence time of the solution. To achieve this goal, a preliminary selection of the design space has been performed by means of an analytical approach. This paper describes a new design criterion based on one dimensional approach. The criterion has been implemented in an in-house tool that takes faster decisions, if compared with a two or a three dimensional model, about the number of possible feasible solutions. During the geometrical optimization phase of the blade fir-tree attachment, in which a parametric model is used, the authors try to handle the geometrical non-feasibility with a combination of Latin Hypercube Sampling (LHS) and an adaptive penalty method. The optimization is done via the genetic algorithm and the computational time of the reduced domain is compared with the original one.
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Briones, Alejandro M., David L. Burrus, Joshua P. Sykes, Brent A. Rankin, and Andrew W. Caswell. "Automated Design Optimization of a Small-Scale High-Swirl Cavity-Stabilized Combustor." In ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-76900.

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A numerical optimization study is performed on a small-scale high-swirl cavity-stabilized combustor. A parametric geometry is created in CAD software that is coupled with meshing software. The latter automatically transfers meshes and boundary conditions to the solver, which is coupled with a post-processing tool. Steady, incompressible three-dimensional simulations are performed using a multi-phase Realizable k-ϵ Reynolds-averaged Navier-Stokes (RANS) approach with the non-adiabatic flamelet progress variable (FPV). There are nine input parameters based on geometrical control variables. There are five output parameters, viz., pattern factor (PF), RMS of the profile factor deviation, averaged exit temperature, averaged exit swirl angle, and total pressure loss. An iterative design of experiments (DOE) with a recursive Latin Hypercube Sampling (LHS) is performed to filter the most important input parameters. The five major input parameters are found with Spearman’s order-rank correlation and R2 coefficient of determination. The five input parameters are used for the adaptive multiple objective (AMO) optimization. The AMO algorithm provided a candidate design point with the lowest weighted objective function. This design point was verified through CFD simulation. The combined filtering and optimization procedures improve the baseline design point in terms of pattern and profile factor. The former halved from that of the baseline design point whereas the latter turned from an outer peak to a center peak profile, closely mimicking an ideal profile. The exit swirl angle favorably increased 25%. The averaged exit temperature and the total pressure losses remained nearly unchanged from the baseline design point.
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Briones, Alejandro M., Markus P. Rumpfkeil, Nathan R. Thomas, and Brent A. Rankin. "Effect of Deterministic and Continuous Design Space Resolution on Multiple-Objective Combustor Optimization." In ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-91388.

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Abstract A supervised machine learning technique namely an Adaptive Multiple Objective (AMO) optimization algorithm is used to divide a continuous and deterministic design space into non-dominated Pareto frontier and dominated design points. The effect of the initial training data quantity, i.e., computational fluid dynamics (CFD) results, on the Pareto frontier and output parameter sensitivity is explored. The optimization study is performed on a subsonic small-scale cavity-stabilized combustor. A parametric geometry is created using CAD that is coupled with a meshing software. The latter automatically transfers meshes and boundary conditions to the solver, which is coupled with a post-processing tool. Steady, incompressible three-dimensional simulations are performed using a multi-phase realizable k-ε Reynolds-averaged Navier-Stokes (RANS) approach with an adiabatic flamelet progress variable (FPV). Scalable wall functions are used for modeling turbulence near the wall. For each CFD simulation four levels of adaptive mesh refinement (AMR) are utilized on the original cut-cell grid. The mesh is refined where the flow exhibits large progress variable curvature. There are fifteen geometrical input parameters and three output parameters, viz., a pattern factor proxy (maximum exit temperature), a combustion efficiency proxy (averaged exit temperature), and total pressure loss (TPL). The Pareto frontier and the input-to-output parameter sensitivities are reported for each meta-model simulation. For the investigated design space, three times the number of input parameters plus one (48) yields an optimization independent of the initial sampling. This conclusion is drawn by comparing the Pareto frontiers and global sensitivities. However, the latter provides a better metric. The relative influence of the input parameters on the outputs is assessed by using both a Spearman’s order-rank correlation approach as well as an active subspace analysis. In general, non-dominated design points exhibit persistent geometrical features such as offset opposed cavity forward and aft driver jet alignment. Larger cavities necessitate larger chutes and smaller outer liner jet diameters, whereas smaller cavities require smaller chutes and larger outer liner jet diameters. The fuel injector radial location varies, but can be located either radially inward or outward with respect to the forward dilution jet radial locations. For these non-dominated designs there is substantial burning inside and outside of the cavity. The downstream dilution jets quench the upstream hot gases.
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Pandita, Piyush, Ilias Bilionis, and Jitesh Panchal. "Extending Expected Improvement for High-Dimensional Stochastic Optimization of Expensive Black-Box Functions." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-60527.

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Design optimization under uncertainty is notoriously difficult when the objective function is expensive to evaluate. State-of-the-art techniques, e.g., stochastic optimization or sampling average approximation, fail to learn exploitable patterns from collected data and, as a result, they tend to require an excessive number of objective function evaluations. There is a need for techniques that alleviate the high cost of information acquisition and select sequential simulations in an optimal way. In the field of deterministic single-objective unconstrained global optimization, the Bayesian global optimization (BGO) approach has been relatively successful in addressing the information acquisition problem. BGO builds a probabilistic surrogate of the expensive objective function and uses it to define an information acquisition function (IAF) whose role is to quantify the merit of making new objective evaluations. Specifically, BGO iterates between making the observations with the largest expected IAF and rebuilding the probabilistic surrogate, until a convergence criterion is met. In this work, we extend the expected improvement (EI) IAF to the case of design optimization under uncertainty. This involves a reformulation of the EI policy that is able to filter out parametric and measurement uncertainties. We by-pass the curse of dimensionality, since the method does not require learning the response surface as a function of the stochastic parameters. To increase the robustness of our approach in the low sample regime, we employ a fully Bayesian interpretation of Gaussian processes by constructing a particle approximation of the posterior of its hyperparameters using adaptive Markov chain Monte Carlo. An addendum of our approach is that it can quantify the epistemic uncertainty on the location of the optimum and the optimal value as induced by the limited number of objective evaluations used in obtaining it. We verify and validate our approach by solving two synthetic optimization problems under uncertainty. We demonstrate our approach by solving a challenging engineering problem: the oil-well-placement problem with uncertainties in the permeability field and the oil price time series.
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