Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINN

DOI10.1016/j.jcp.2022.111173OpenAlexW4225708642MaRDI QIDQ2137979

Yating Wang, Zecheng Zhang, Guang Lin

Publication date: 11 May 2022

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

Full work available at URL: https://arxiv.org/abs/2107.06330

zbMATH Keywords

inverse problemneural networkmachine learningdeep learningreplica exchangeBayesian physics-informed neural network

Mathematics Subject Classification ID

Numerical methods for ordinary differential equations (65Lxx) Markov processes (60Jxx) Probabilistic methods, stochastic differential equations (65Cxx)

Related Items (1)

Adaptive weighting of Bayesian physics informed neural networks for multitask and multiscale forward and inverse problems

Cites Work

• Unnamed Item
• An adaptive importance sampling algorithm for Bayesian inversion with multimodal distributions
• Langevin diffusions and Metropolis-Hastings algorithms
• B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
• Bayesian sparse learning with preconditioned stochastic gradient MCMC and its applications
• An adaptive Hessian approximated stochastic gradient MCMC method
• On the eigenvector bias of Fourier feature networks: from regression to solving multi-scale PDEs with physics-informed neural networks
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• (Non-) asymptotic properties of Stochastic Gradient Langevin Dynamics
• Inverse problems: A Bayesian perspective
• Preconditioning Markov Chain Monte Carlo Simulations Using Coarse-Scale Models

This page was built for publication: Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINN