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Mean flow reconstruction of unsteady flows using physics-informed neural networks

https://doi.org/10.1017/dce.2022.37

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Bayesian Physics Informed Neural Networks for real-world nonlinear dynamical systems

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Nektar++: An open-source spectral/ h p element framework

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Parallel physics-informed neural networks via domain decomposition

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A data-assimilation method for Reynolds-averaged Navier–Stokes-driven mean flow reconstruction

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Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

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Mean-flow data assimilation based on minimal correction of turbulence models: Application to turbulent high Reynolds number backward-facing step

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Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

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DeepXDE: A Deep Learning Library for Solving Differential Equations

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Machine Learning for Fluid Mechanics

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Extended Physics-Informed Neural Networks (XPINNs): A Generalized Space-Time Domain Decomposition Based Deep Learning Framework for Nonlinear Partial Differential Equations

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