Literatura académica sobre el tema "Adaptive parametric sampling"
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Artículos de revistas sobre el tema "Adaptive parametric sampling"
Rafiq, Danish y Mohammad Abid Bazaz. "Adaptive parametric sampling scheme for nonlinear model order reduction". Nonlinear Dynamics 107, n.º 1 (2 de noviembre de 2021): 813–28. http://dx.doi.org/10.1007/s11071-021-07025-7.
Texto completoAzencott, R., A. Beri y I. Timofeyev. "Adaptive Sub-sampling for Parametric Estimation of Gaussian Diffusions". Journal of Statistical Physics 139, n.º 6 (1 de mayo de 2010): 1066–89. http://dx.doi.org/10.1007/s10955-010-9975-y.
Texto completoBorggaard, Jeff, Kevin R. Pond y Lizette Zietsman. "Parametric Reduced Order Models Using Adaptive Sampling and Interpolation". IFAC Proceedings Volumes 47, n.º 3 (2014): 7773–78. http://dx.doi.org/10.3182/20140824-6-za-1003.02664.
Texto completoLiu, Ying, Hongguang Li, Huanyu Du, Ningke Tong y Guang Meng. "An adaptive sampling procedure for parametric model order reduction by matrix interpolation". Journal of Low Frequency Noise, Vibration and Active Control 39, n.º 4 (15 de junio de 2019): 821–34. http://dx.doi.org/10.1177/1461348419851595.
Texto completoChen, Yi-Wen y 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, n.º 1 (enero de 2012): 113–27. http://dx.doi.org/10.1109/tcsvt.2011.2158341.
Texto completoWoudt, Edwin, Pieter-Tjerk de Boer y Jan-Kees van Ommeren. "Improving Adaptive Importance Sampling Simulation of Markovian Queueing Models using Non-parametric Smoothing". SIMULATION 83, n.º 12 (diciembre de 2007): 811–20. http://dx.doi.org/10.1177/0037549707087223.
Texto completoJia, Gaofeng y Alexandros A. Taflanidis. "Non-parametric stochastic subset optimization utilizing multivariate boundary kernels and adaptive stochastic sampling". Advances in Engineering Software 89 (noviembre de 2015): 3–16. http://dx.doi.org/10.1016/j.advengsoft.2015.06.014.
Texto completoLu, Kuan, Haopeng Zhang, Kangyu Zhang, Yulin Jin, Shibo Zhao, Chao Fu y Yushu Chen. "The Transient POD Method Based on Minimum Error of Bifurcation Parameter". Mathematics 9, n.º 4 (16 de febrero de 2021): 392. http://dx.doi.org/10.3390/math9040392.
Texto completoOurbih-Tari, Megdouda y Mahdia Azzal. "Survival function estimation with non parametric adaptive refined descriptive sampling algorithm: A case study". Communications in Statistics - Theory and Methods 46, n.º 12 (25 de abril de 2016): 5840–50. http://dx.doi.org/10.1080/03610926.2015.1065328.
Texto completoMorio, Jérôme. "Non-parametric adaptive importance sampling for the probability estimation of a launcher impact position". Reliability Engineering & System Safety 96, n.º 1 (enero de 2011): 178–83. http://dx.doi.org/10.1016/j.ress.2010.08.006.
Texto completoTesis sobre el tema "Adaptive parametric sampling"
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.
Texto completoThe 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
Castro, Rui M. "Active learning and adaptive sampling for non-parametric inference". Thesis, 2008. http://hdl.handle.net/1911/22265.
Texto completoCapítulos de libros sobre el tema "Adaptive parametric sampling"
Quinn, J. A., F. C. Langbein, R. R. Martin y G. Elber. "Density-Controlled Sampling of Parametric Surfaces Using Adaptive Space-Filling Curves". En Geometric Modeling and Processing - GMP 2006, 465–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11802914_33.
Texto completoFigueiredo, Luiz Henrique de. "Adaptive Sampling of Parametric Curves". En Graphics Gems V, 173–78. Elsevier, 1995. http://dx.doi.org/10.1016/b978-0-12-543457-7.50032-2.
Texto completoActas de conferencias sobre el tema "Adaptive parametric sampling"
Varona, Maria Cruz, Mashuq-un-Nabiz y Boris Lohmann. "Automatic adaptive sampling in parametric Model Order Reduction by Matrix Interpolation". En 2017 IEEE International Conference on Advanced Intelligent Mechatronics (AIM). IEEE, 2017. http://dx.doi.org/10.1109/aim.2017.8014062.
Texto completoHsu, Charles y Harold Szu. "Low-discrepancy sampling of parametric surface using adaptive space-filling curves (SFC)". En SPIE Sensing Technology + Applications, editado por Harold H. Szu y Liyi Dai. SPIE, 2014. http://dx.doi.org/10.1117/12.2053306.
Texto completoHombal, Vadiraj, Arthur Sanderson y Richard Blidberg. "A Non-Parametric Iterative Algorithm For Adaptive Sampling And Robotic Vehicle Path Planning". En 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2006. http://dx.doi.org/10.1109/iros.2006.282561.
Texto completoJi, Runda y Qiqi Wang. "Aerodynamic Risk Assessment using Parametric, Three-Dimensional Unstructured, High-Fidelity CFD and Adaptive Sampling". En AIAA AVIATION 2021 FORUM. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2021. http://dx.doi.org/10.2514/6.2021-2461.
Texto completoMa, Jian-Wei, De-Ning Song, Zhen-Yuan Jia, Ning Zhang, Guo-Qing Hu y Wei-Wei Su. "Adaptive Pre-Compensation of the Contouring Error for High-Precision Parametric Curved Contour Following". En ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-71284.
Texto completoWeaver-Rosen, Jonathan M. y Richard J. Malak. "Efficient Parametric Optimization for Expensive Single Objective Problems". En 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.
Texto completoAlinejad, F., D. Botto, M. Gola y A. Bessone. "Reduction of the Design Space to Optimize Blade Fir-Tree Attachments". En ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-75781.
Texto completoBriones, Alejandro M., David L. Burrus, Joshua P. Sykes, Brent A. Rankin y Andrew W. Caswell. "Automated Design Optimization of a Small-Scale High-Swirl Cavity-Stabilized Combustor". En ASME Turbo Expo 2018: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/gt2018-76900.
Texto completoBriones, Alejandro M., Markus P. Rumpfkeil, Nathan R. Thomas y Brent A. Rankin. "Effect of Deterministic and Continuous Design Space Resolution on Multiple-Objective Combustor Optimization". En ASME Turbo Expo 2019: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/gt2019-91388.
Texto completoPandita, Piyush, Ilias Bilionis y Jitesh Panchal. "Extending Expected Improvement for High-Dimensional Stochastic Optimization of Expensive Black-Box Functions". En 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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