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Physics-informed neural networks (PINNs) for fluid mechanics: A review
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Physics-informed neural networks (PINNs) for fluid mechanics: A review
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Despite the significant progress over the last 50 years in simulating flow problems using numerical discretization of the Navier-Stokes equations (NSE), we still cannot incorporate seamlessly noisy data into existing algorithms, mesh-generation is complex, and we cannot tackle high-dimensional problems governed by parametrized NSE. Moreover, solving inverse flow problems is often prohibitively expensive and requires complex and expensive formulations and new computer codes. Here, we review flow physics-informed learning, integrating seamlessly data and mathematical models, and implementing them using physics-informed neural networks (PINNs). We demonstrate the effectiveness of PINNs for inverse problems related to three-dimensional wake flows, supersonic flows, and biomedical flows.
Forward citations
Cited by 4 Pith papers
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DSPR: Dual-Stream Physics-Residual Networks for Trustworthy Industrial Time Series Forecasting
DSPR decouples statistical temporal evolution from physics-informed residual dynamics via an adaptive window for transport delays and a physics-guided dynamic graph to achieve accurate, physically plausible forecasts ...
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Solving Hamiltonian Constraint Equation with Physics-Informed Neural Networks
PINNs with specialized techniques solve the nonlinear Hamiltonian constraint for generic binary black hole initial data, matching traditional NR accuracy.
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DSPR: Dual-Stream Physics-Residual Networks for Trustworthy Industrial Time Series Forecasting
DSPR decouples temporal patterns and residual dynamics with physics priors to improve accuracy and plausibility in non-stationary industrial forecasting.
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Deep learning applied to computational mechanics: A comprehensive review, state of the art, and the classics
A comprehensive review of deep learning techniques for computational mechanics, including LSTM for constitutive modeling, PINNs for PDE solving, optimizers, and kernel methods.
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