Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning

12 April 2024

Papers citing "Leveraging viscous Hamilton-Jacobi PDEs for uncertainty quantification in scientific machine learning"

8 / 8 papers shown

Title
Learning and discovering multiple solutions using physics-informed neural networks with random initialization and deep ensemble Zongren Zou Zhicheng Wang George Karniadakis PINN AI4CE 65 2 0 08 Mar 2025
HJ-sampler: A Bayesian sampler for inverse problems of a stochastic process by leveraging Hamilton-Jacobi PDEs and score-based generative models Tingwei Meng Zongren Zou Jérome Darbon George Karniadakis DiffM 37 2 0 15 Sep 2024
Quantification of total uncertainty in the physics-informed reconstruction of CVSim-6 physiology Mario De Florio Zongren Zou Daniele E. Schiavazzi George Karniadakis 26 3 0 13 Aug 2024
NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements K. Shukla Zongren Zou Chi Hin Chan Additi Pandey Zhicheng Wang George Karniadakis PINN 45 7 0 30 Jul 2024
A comprehensive and FAIR comparison between MLP and KAN representations for differential equations and operator networks K. Shukla Juan Diego Toscano Zhicheng Wang Zongren Zou George Karniadakis 26 74 0 05 Jun 2024
Large scale scattering using fast solvers based on neural operators Zongren Zou Adar Kahana Enrui Zhang Eli Turkel Rishikesh Ranade Jay Pathak George Karniadakis 34 1 0 20 May 2024
B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Data Liu Yang Xuhui Meng George Karniadakis PINN 172 758 0 13 Mar 2020
MCMC using Hamiltonian dynamics Radford M. Neal 173 3,260 0 09 Jun 2012