End-to-End Mesh Optimization of a Hybrid Deep Learning Black-Box PDE Solver

arXiv:2404.11766v1 Announce Type: new
Abstract: Deep learning has been widely applied to solve partial differential equations (PDEs) in computational fluid dynamics. Recent research proposed a PDE correction framework that leverages deep learning to correct the solution obtained by a PDE solver on a coarse mesh. However, end-to-end training of such a PDE correction model over both solver-dependent parameters such as mesh parameters and neural network parameters requires the PDE solver to support automatic differentiation through the iterative numerical process. Such …

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