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Title: Correcting model misspecification in physics-informed neural networks (PINNs)

Journal Article · · Journal of Computational Physics

Sponsoring Organization:
USDOE
OSTI ID:
2323588
Journal Information:
Journal of Computational Physics, Journal Name: Journal of Computational Physics Vol. 505 Journal Issue: C; ISSN 0021-9991
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

References (32)

Physics-informed neural networks (PINNs) for fluid mechanics: a review journal December 2021
DISCRETE- vs. CONTINUOUS-TIME NONLINEAR SIGNAL PROCESSING OF Cu ELECTRODISSOLUTION DATA journal November 1992
NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators journal February 2024
Distilling Free-Form Natural Laws from Experimental Data journal April 2009
Model Uncertainty in Climate Change Economics: A Review and Proposed Framework for Future Research journal September 2020
DeepXDE: A Deep Learning Library for Solving Differential Equations journal January 2021
Discovering a reaction–diffusion model for Alzheimer’s disease by combining PINNs with symbolic regression journal February 2024
Conservative physics-informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems journal June 2020
The benefits of quantifying climate model uncertainty in climate change impacts assessment: an example with heat-related mortality change estimates journal August 2011
Significant impact of forcing uncertainty in a large ensemble of climate model simulations journal May 2021
hp-VPINNs: Variational physics-informed neural networks with domain decomposition journal February 2021
Physics-informed learning of governing equations from scarce data journal October 2021
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations journal February 2019
Uncertainty quantification in scientific machine learning: Methods, metrics, and comparisons journal March 2023
DAS-PINNs: A deep adaptive sampling method for solving high-dimensional partial differential equations journal March 2023
Discovering governing equations from data by sparse identification of nonlinear dynamical systems journal March 2016
A generative modeling framework for inferring families of biomechanical constitutive laws in data-sparse regimes journal December 2023
Identification of distributed parameter systems: A neural net based approach journal March 1998
A Localized Mass-Conserving Lattice Boltzmann Approach for Non-Newtonian Fluid Flows journal April 2015
B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data journal January 2021
Physics-Informed Neural Networks for Brain Hemodynamic Predictions Using Medical Imaging journal September 2022
Data-driven discovery of partial differential equations journal April 2017
Data-driven discovery of governing equations for fluid dynamics based on molecular simulation journal March 2020
Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINN journal July 2022
Multi-fidelity Bayesian neural networks: Algorithms and applications journal August 2021
On generalized residual network for deep learning of unknown dynamical systems journal August 2021
Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems journal December 2022
Monte Carlo fPINNs: Deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations journal October 2022
fPINNs: Fractional Physics-Informed Neural Networks journal January 2019
NH-PINN: Neural homogenization-based physics-informed neural network for multiscale problems journal December 2022
Failure-Informed Adaptive Sampling for PINNs journal July 2023
GFINNs: GENERIC formalism informed neural networks for deterministic and stochastic dynamical systems
  • Zhang, Zhen; Shin, Yeonjong; Em Karniadakis, George
  • Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, Vol. 380, Issue 2229 https://doi.org/10.1098/rsta.2021.0207
journal June 2022