Physics-informed Information Field Theory for Modeling Physical Systems with Uncertainty Quantification

18 January 2023

Papers citing "Physics-informed Information Field Theory for Modeling Physical Systems with Uncertainty Quantification"

8 / 8 papers shown

Title
DGenNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Yaohua Zang P. Koutsourelakis AI4CE 76 1 0 10 Feb 2025
Information Field Theory and Artificial Intelligence T. Ensslin 37 4 0 19 Dec 2021
Physics-Informed Generative Adversarial Networks for Stochastic Differential Equations Siyu Dai Shawn Schaffert Andreas G. Hofmann 99 359 0 05 Nov 2018
Quantifying total uncertainty in physics-informed neural networks for solving forward and inverse stochastic problems Dongkun Zhang Lu Lu Ling Guo George Karniadakis UQCV 98 405 0 21 Sep 2018
Discovering physical concepts with neural networks Raban Iten Tony Metger H. Wilming L. D. Rio R. Renner PINN AI4CE 51 387 0 26 Jul 2018
Hidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations M. Raissi George Karniadakis AI4CE PINN 58 1,134 0 02 Aug 2017
Stochastic Gradient Hamiltonian Monte Carlo Tianqi Chen E. Fox Carlos Guestrin BDL 88 906 0 17 Feb 2014
The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo Matthew D. Hoffman Andrew Gelman 147 4,275 0 18 Nov 2011