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Mosaic Flows: A Transferable Deep Learning Framework for Solving PDEs on Unseen Domains
[article]
2021
arXiv
pre-print
Physics-informed neural networks (PINNs) are increasingly employed to replace/augment traditional numerical methods in solving partial differential equations (PDEs). While state-of-the-art PINNs have many attractive features, they approximate a specific realization of a PDE system and hence are problem-specific. That is, the model needs to be re-trained each time the boundary conditions (BCs) and domain shape/size change. This limitation prohibits the application of PINNs to realistic or
arXiv:2104.10873v2
fatcat:yvvxu7mh5fgwlnxkcl5be2u6mi