Polynomial-Chaos-based Kriging

13 February 2015

Papers citing "Polynomial-Chaos-based Kriging"

11 / 11 papers shown

Title
Uncertainty Quantification in Machine Learning for Engineering Design and Health Prognostics: A Tutorial V. Nemani Luca Biggio Xun Huan Zhen Hu Olga Fink Anh Tran Yan Wang Xiaoge Zhang Chao Hu AI4CE 33 75 0 07 May 2023
Active learning for structural reliability analysis with multiple limit state functions through variance-enhanced PC-Kriging surrogate models A. J.Moran P. G. Morato P. Rigo AI4CE 14 0 0 23 Feb 2023
Multielement polynomial chaos Kriging-based metamodelling for Bayesian inference of non-smooth systems J. C. García-Merino C. Calvo-Jurado E. Martínez-Paneda E. García-Macías 17 10 0 05 Dec 2022
Recent Advances in Uncertainty Quantification Methods for Engineering Problems Dinesh Kumar Farid Ahmed S. Usman A. Alajo S. B. Alam 11 7 0 06 Nov 2022
Accelerating hypersonic reentry simulations using deep learning-based hybridization (with guarantees) Paul Novello Gaël Poëtte D. Lugato S. Peluchon P. Congedo AI4CE 19 7 0 27 Sep 2022
A connection between probability, physics and neural networks Sascha Ranftl PINN 17 9 0 26 Sep 2022
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty Paz Fink Shustin Shashanka Ubaru Vasileios Kalantzis L. Horesh H. Avron 23 2 0 10 Feb 2022
Surrogate-Based Bayesian Inverse Modeling of the Hydrological System: An Adaptive Approach Considering Surrogate Approximation Error Jiangjiang Zhang Q. Zheng Dingjiang Chen Laosheng Wu L. Zeng 19 36 0 10 Jul 2018
Metamodel-based sensitivity analysis: Polynomial chaos expansions and Gaussian processes Loic Le Gratiet S. Marelli Bruno Sudret 26 157 0 14 Jun 2016
Sparse polynomial chaos expansions of frequency response functions using stochastic frequency transformation V. Yaghoubi S. Marelli Bruno Sudret T. Abrahamsson 9 52 0 06 Jun 2016
Asymptotic analysis of the role of spatial sampling for covariance parameter estimation of Gaussian processes F. Bachoc 53 57 0 18 Jan 2013