Bayesian Hidden Physics Models: Uncertainty Quantification for Discovery of Nonlinear Partial Differential Operators from Data

7 June 2020

Papers citing "Bayesian Hidden Physics Models: Uncertainty Quantification for Discovery of Nonlinear Partial Differential Operators from Data"

4 / 4 papers shown

Title
Machine Learning for Partial Differential Equations Steven L. Brunton J. Nathan Kutz AI4CE 45 20 0 30 Mar 2023
Discovering Governing Equations from Partial Measurements with Deep Delay Autoencoders Joseph Bakarji Kathleen P. Champion J. Nathan Kutz Steven L. Brunton 40 82 0 13 Jan 2022
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 183 761 0 13 Mar 2020
Learning Symbolic Physics with Graph Networks M. Cranmer Rui Xu Peter W. Battaglia S. Ho PINN AI4CE 191 84 0 12 Sep 2019