APIK: Active Physics-Informed Kriging Model with Partial Differential
Equations

APIK: Active Physics-Informed Kriging Model with Partial Differential Equations

22 December 2020

Zhehui Chen

Papers citing "APIK: Active Physics-Informed Kriging Model with Partial Differential Equations"

8 / 8 papers shown

Title
Stochastic PDE representation of random fields for large-scale Gaussian process regression and statistical finite element analysis Kim Jie Koh F. Cirak AI4CE 24 9 0 23 May 2023
Gaussian Process Priors for Systems of Linear Partial Differential Equations with Constant Coefficients Marc Härkönen Markus Lange-Hegermann Bogdan Raiță 32 15 0 29 Dec 2022
Parameter Inference based on Gaussian Processes Informed by Nonlinear Partial Differential Equations Zhao-Xia Li Shih-Feng Yang Jeff Wu 11 2 0 22 Dec 2022
PAGP: A physics-assisted Gaussian process framework with active learning for forward and inverse problems of partial differential equations Jiahao Zhang Shiqi Zhang Guang Lin 43 3 0 06 Apr 2022
Bayesian Calibration of Imperfect Computer Models using Physics-Informed Priors Michail Spitieris I. Steinsland AI4CE 28 6 0 17 Jan 2022
Failure-averse Active Learning for Physics-constrained Systems Cheolhei Lee Xing Wang Jianguo Wu Xiaowei Yue AI4CE 19 7 0 27 Oct 2021
Bayesian Numerical Methods for Nonlinear Partial Differential Equations Junyang Wang Jon Cockayne O. Chkrebtii T. Sullivan Chris J. Oates 59 19 0 22 Apr 2021
Local Gaussian process approximation for large computer experiments R. Gramacy D. Apley 127 392 0 02 Mar 2013