Physics-informed neural networks for data-driven simulation: advantages, limitations, and opportunities

DOI10.1016/j.physa.2022.128415OpenAlexW4313551293MaRDI QIDQ2683126

Félix Fernández de la Mata, Alfonso Gijón, Miguel Molina-Solana, Juan Gómez-Romero

Publication date: 3 February 2023

Published in: Physica A (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.physa.2022.128415

zbMATH Keywords

deep learningphysics-informed neural networksdata-driven simulationslearned simulators

Mathematics Subject Classification ID

Statistical mechanics, structure of matter (82-XX)

Uses Software

• FreeFem++
• SciPy
• Python
• DeepXDE
• SciANN
• IDRLnet
• SimNet
• FPINNs

Cites Work

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• Discovering governing equations from data by sparse identification of nonlinear dynamical systems
• SciANN: a Keras/Tensorflow wrapper for scientific computations and physics-informed deep learning using artificial neural networks
• The neural particle method - an updated Lagrangian physics informed neural network for computational fluid dynamics
• Simple computational strategies for more effective physics-informed neural networks modeling of turbulent natural convection
• Physics-informed multi-LSTM networks for metamodeling of nonlinear structures
• Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
• New development in freefem++
• DeepXDE: A Deep Learning Library for Solving Differential Equations
• On the Convergence of Physics Informed Neural Networks for Linear Second-Order Elliptic and Parabolic Type PDEs
• Data-driven discovery of coordinates and governing equations
• fPINNs: Fractional Physics-Informed Neural Networks

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