lu-group 的其他开源项目
A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data
Non-adaptive and residual-based adaptive sampling for PINNs
Multifidelity deep neural operators for efficient learning of partial differential equations with application to fast inverse design of nanoscale heat transport
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A collection of Jupyter notebooks providing tutorials on reduced order modeling techniques like DeepONet, FNO, DL-ROM, and POD-DL-ROM. Easily runnable on Google Colab.
Python script to run CFD analysis on airfoil using **OpenFOAM** to simulate and **gmsh** to generate mesh.
Must-read Papers on Physics-Informed Neural Networks.
Computational Fluid Dynamics (CFD) for FreeCAD based on OpenFOAM solver
#计算机科学#Physics-informed neural network for solving fluid dynamics problems
TensorFlow 2.0 implementation of Maziar Raissi's Physics Informed Neural Networks (PINNs).
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A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data
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