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"

7 / 7 papers shown

Title
DGNO: A Novel Physics-aware Neural Operator for Solving Forward and Inverse PDE Problems based on Deep, Generative Probabilistic Modeling Yaohua Zang P. Koutsourelakis AI4CE 52 0 0 10 Feb 2025
Auto-weighted Bayesian Physics-Informed Neural Networks and robust estimations for multitask inverse problems in pore-scale imaging of dissolution S. Pérez P. Poncet 17 3 0 24 Aug 2023
An information field theory approach to Bayesian state and parameter estimation in dynamical systems Kairui Hao Ilias Bilionis 4 4 0 03 Jun 2023
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 170 755 0 13 Mar 2020
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning Y. Gal Zoubin Ghahramani UQCV BDL 247 9,042 0 06 Jun 2015
NIFTY - Numerical Information Field Theory - a versatile Python library for signal inference M. Selig M. Bell H. Junklewitz N. Oppermann M. Reinecke M. Greiner C. Pachajoa T. Ensslin 55 62 0 18 Jan 2013
MCMC using Hamiltonian dynamics Radford M. Neal 132 3,260 0 09 Jun 2012