Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation

5 July 2024

Papers citing "Randomized Physics-Informed Neural Networks for Bayesian Data Assimilation"

13 / 13 papers shown

Title
Randomized Physics-Informed Machine Learning for Uncertainty Quantification in High-Dimensional Inverse Problems Yifei Zong D. Barajas-Solano A. Tartakovsky 63 2 0 11 Dec 2023
Bayesian Physics-Informed Neural Networks for real-world nonlinear dynamical systems K. Linka Amelie Schäfer Xuhui Meng Zongren Zou George Karniadakis E. Kuhl OOD PINN AI4CE 75 114 0 12 May 2022
What Are Bayesian Neural Network Posteriors Really Like? Pavel Izmailov Sharad Vikram Matthew D. Hoffman A. Wilson UQCV BDL 72 385 0 29 Apr 2021
When and why PINNs fail to train: A neural tangent kernel perspective Sizhuang He Xinling Yu P. Perdikaris 141 914 0 28 Jul 2020
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 233 786 0 13 Mar 2020
Physics-Informed Neural Networks for Multiphysics Data Assimilation with Application to Subsurface Transport Qizhi He D. Barajas-Solano G. Tartakovsky A. Tartakovsky AI4CE 52 262 0 06 Dec 2019
Quality of Uncertainty Quantification for Bayesian Neural Network Inference Jiayu Yao Weiwei Pan S. Ghosh Finale Doshi-Velez UQCV BDL 179 113 0 24 Jun 2019
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm Qiang Liu Dilin Wang BDL 73 1,092 0 16 Aug 2016
A randomized maximum a posterior method for posterior sampling of high dimensional nonlinear Bayesian inverse problems Kainan Wang T. Bui-Thanh Omar Ghattas 38 46 0 11 Feb 2016
Variational Inference: A Review for Statisticians David M. Blei A. Kucukelbir Jon D. McAuliffe BDL 285 4,793 0 04 Jan 2016
Automatic differentiation in machine learning: a survey A. G. Baydin Barak A. Pearlmutter Alexey Radul J. Siskind PINN AI4CE ODL 166 2,808 0 20 Feb 2015
MCMC using Hamiltonian dynamics Radford M. Neal 292 3,279 0 09 Jun 2012
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Matthew D. Hoffman Andrew Gelman 166 4,304 0 18 Nov 2011