Relevant bibliographies by topics / Adaptive parametric sampling

Contents

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

Academic literature on the topic 'Adaptive parametric sampling'

Author: Grafiati
Published: 29 August 2023
Last updated: 31 July 2025

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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 (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 (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 (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 r
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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 (2012): 113–27. http://dx.doi.org/10.1109/tcsvt.2011.2158341.

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Lu, Kuan, Haopeng Zhang, Kangyu Zhang, et al. "The Transient POD Method Based on Minimum Error of Bifurcation Parameter." Mathematics 9, no. 4 (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
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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 (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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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 (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 (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 quies
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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 obs
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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. 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. Elsevier, 1995. http://dx.doi.org/10.1016/b978-0-12-543457-7.50032-2.

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West, Mike. "Modelling with Mixtures." In Bayesian Statistics 4. Oxford University PressOxford, 1992. http://dx.doi.org/10.1093/oso/9780198522669.003.0028.

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Abstract Discrete mixtures of distributions of standard parametric forms commonly arise in statistical modelling and with methods of analysis that exploit mixture structure. This paper discusses general issues of modelling with mixtures that arise in fitting mixtures to data distributions, using mixtures to approximate functional forms, such as posterior distributions in parametric models, and development of mixture pruning methods useful for reducing the number of components of large mixtures. These issues arise in problems of density estimation using mixtures of Dirichlet processes, adaptive
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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. 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 adap
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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
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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
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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
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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 aut
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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 globa
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