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

Artificial intelligence (68Txx) Miscellaneous topics in partial differential equations (35Rxx) Probabilistic methods, stochastic differential equations (65Cxx)

Cites Work

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Discovering governing equations from data by sparse identification of nonlinear dynamical systems
\textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition
Monte Carlo fPINNs: deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations
NH-PINN: neural homogenization-based physics-informed neural network for multiscale problems
B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data
Multi-fidelity Bayesian neural networks: algorithms and applications
On generalized residual network for deep learning of unknown dynamical systems
Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINN
Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems
Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Bayesian physics informed neural networks for real-world nonlinear dynamical systems
DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations
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DeepXDE: A Deep Learning Library for Solving Differential Equations
Data-driven discovery of governing equations for fluid dynamics based on molecular simulation
fPINNs: Fractional Physics-Informed Neural Networks
A Localized Mass-Conserving Lattice Boltzmann Approach for Non-Newtonian Fluid Flows
A framework based on symbolic regression coupled with eXtended physics-informed neural networks for gray-box learning of equations of motion from data
NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators
Failure-Informed Adaptive Sampling for PINNs
Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression
Discrepancy Modeling Framework: Learning Missing Physics, Modeling Systematic Residuals, and Disambiguating between Deterministic and Random Effects

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