Correcting model misspecification in physics-informed neural networks (PINNs) (Q6497270)

scientific article; zbMATH DE number 7842894

Language	Label	Description	Also known as
English	Correcting model misspecification in physics-informed neural networks (PINNs)	scientific article; zbMATH DE number 7842894

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instance of

scholarly article

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title

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

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author

Zongren Zou

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Xuhui Meng

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George Em. Karniadakis

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published in

Journal of Computational Physics

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publication date

6 May 2024

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zbMATH Keywords

model uncertainty

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physics-informed neural networks

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model misspecification

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uncertainty quantification

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non-Newtonian flows

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symbolic regression

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MaRDI profile type

MaRDI publication profile

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cites work

Data-driven discovery of governing equations for fluid dynamics based on molecular simulation

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Discovering governing equations from data by sparse identification of nonlinear dynamical systems

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Robust data-driven discovery of governing physical laws with error bars

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Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

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Discrepancy Modeling Framework: Learning Missing Physics, Modeling Systematic Residuals, and Disambiguating between Deterministic and Random Effects

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On generalized residual network for deep learning of unknown dynamical systems

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Discovering a reaction-diffusion model for Alzheimer's disease by combining PINNs with symbolic regression

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Uncertainty quantification in scientific machine learning: methods, metrics, and comparisons

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NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators

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B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data

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DeepXDE: A Deep Learning Library for Solving Differential Equations

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fPINNs: Fractional Physics-Informed Neural Networks

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\textit{hp}-VPINNs: variational physics-informed neural networks with domain decomposition

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Conservative physics-informed neural networks on discrete domains for conservation laws: applications to forward and inverse problems

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Bayesian physics informed neural networks for real-world nonlinear dynamical systems

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Multi-variance replica exchange SGMCMC for inverse and forward problems via Bayesian PINN

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NH-PINN: neural homogenization-based physics-informed neural network for multiscale problems

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DAS-PINNs: a deep adaptive sampling method for solving high-dimensional partial differential equations

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Multi-fidelity Bayesian neural networks: algorithms and applications

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Monte Carlo fPINNs: deep learning method for forward and inverse problems involving high dimensional fractional partial differential equations

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Failure-Informed Adaptive Sampling for PINNs

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A framework based on symbolic regression coupled with eXtended physics-informed neural networks for gray-box learning of equations of motion from data

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A Localized Mass-Conserving Lattice Boltzmann Approach for Non-Newtonian Fluid Flows

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Q3172405

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Identifiers

Mathematics Subject Classification ID

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10.1016/J.JCP.2024.112918

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Sitelinks

Mathematics(1 entry)

mardi Publication:6497270