Polynomial-Chaos-based Kriging

13 February 2015

Papers citing "Polynomial-Chaos-based Kriging"

24 / 24 papers shown

Title
Spline Dimensional Decomposition with Interpolation-based Optimal Knot Selection for Stochastic Dynamic Analysis Yeonsu Kim Junhan Lee John T. Hwang Bingran Wang Dongjin Lee 39 0 0 19 May 2025
Surrogate modeling for probability distribution estimation:uniform or adaptive design? Maijia Su Ziqi Wang O. Bursi M. Broccardo 41 2 0 10 Apr 2024
Reliability analysis of arbitrary systems based on active learning and global sensitivity analysis M. Moustapha Pietro Parisi S. Marelli Bruno Sudret 18 10 0 31 May 2023
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 113 81 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 36 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 47 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 111 8 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 62 8 0 27 Sep 2022
A connection between probability, physics and neural networks Sascha Ranftl PINN 70 9 0 26 Sep 2022
Machine Learning in Aerodynamic Shape Optimization Ji-chao Li Xiaosong Du J. Martins AI4CE 91 194 0 15 Feb 2022
State-of-the-Art Review of Design of Experiments for Physics-Informed Deep Learning Sourav Das S. Tesfamariam PINN AI4CE 79 20 0 13 Feb 2022
PCENet: High Dimensional Surrogate Modeling for Learning Uncertainty Paz Fink Shustin Shashanka Ubaru Vasileios Kalantzis L. Horesh H. Avron 58 2 0 10 Feb 2022
Deep convolutional neural network for shape optimization using level-set approach Wrik Mallik N. Farvolden J. Jelovica R. Jaiman 29 3 0 17 Jan 2022
Gradient-enhanced multifidelity neural networks for high-dimensional function approximation J. Nagawkar Leifur Þ. Leifsson 27 0 0 23 Mar 2021
Simulation free reliability analysis: A physics-informed deep learning based approach S. Chakraborty AI4CE 51 16 0 04 May 2020
Stochastic spectral embedding S. Marelli Paul Wagner C. Lataniotis Bruno Sudret 60 23 0 09 Apr 2020
Sparse Polynomial Chaos Expansions: Literature Survey and Benchmark Nora Lüthen S. Marelli Bruno Sudret 78 156 0 04 Feb 2020
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 88 36 0 10 Jul 2018
On efficient global optimization via universal Kriging surrogate models P. Palar K. Shimoyama 14 42 0 23 Mar 2018
Gradient-based Optimization for Regression in the Functional Tensor-Train Format Alex A. Gorodetsky J. Jakeman 69 34 0 03 Jan 2018
The Gaussian process modelling module in UQLab C. Lataniotis S. Marelli Bruno Sudret GP 20 11 0 27 Sep 2017
Hierarchical Kriging for multi-fidelity aero-servo-elastic simulators - Application to extreme loads on wind turbines Imad Abdallah S. Marelli Bruno Sudret 44 32 0 22 Sep 2017
Metamodel-based sensitivity analysis: Polynomial chaos expansions and Gaussian processes Loic Le Gratiet S. Marelli Bruno Sudret 66 157 0 14 Jun 2016
Sparse polynomial chaos expansions of frequency response functions using stochastic frequency transformation V. Yaghoubi S. Marelli Bruno Sudret T. Abrahamsson 25 52 0 06 Jun 2016