Sparse Gaussian processes for solving nonlinear PDEs

DOI10.1016/j.jcp.2023.112340arXiv2205.03760OpenAlexW4383198146MaRDI QIDQ6173368

Xianjin Yang, Rui Meng

Publication date: 21 July 2023

Published in: Journal of Computational Physics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/2205.03760

zbMATH Keywords

partial differential equationssparse Gaussian process

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Game theory (91Axx) Numerical methods for partial differential equations, initial value and time-dependent initial-boundary value problems (65Mxx)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Weak adversarial networks for high-dimensional partial differential equations
• Geometry on probability spaces
• Bayesian numerical methods for nonlinear partial differential equations
• Solving and learning nonlinear PDEs with Gaussian processes
• Numerical methods for mean field games based on Gaussian processes and Fourier features
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• Shannon sampling. II: Connections to learning theory
• Multigrid with Rough Coefficients and Multiresolution Operator Decomposition from Hierarchical Information Games
• PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification
• Numerical Gaussian Processes for Time-Dependent and Nonlinear Partial Differential Equations
• Proximal Methods for Stationary Mean Field Games with Local Couplings
• Operator-Adapted Wavelets, Fast Solvers, and Numerical Homogenization
• Sparse Cholesky Factorization by Kullback--Leibler Minimization
• Bayesian Numerical Homogenization
• Improved Bounds for the Nyström Method With Application to Kernel Classification
• Mean Field Games: Numerical Methods

This page was built for publication: Sparse Gaussian processes for solving nonlinear PDEs