Corpus ID: 220280657

Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers

@article{Um2020SolverintheLoopLF,
  title={Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers},
  author={Kiwon Um and Yun Fei and Philipp Holl and Robert Brand and N. Th{\"u}rey},
  journal={ArXiv},
  year={2020},
  volume={abs/2007.00016}
}
Finding accurate solutions to partial differential equations (PDEs) is a crucial task in all scientific and engineering disciplines. It has recently been shown that machine learning methods can improve the solution accuracy by correcting for effects not captured by the discretized PDE. We target the problem of reducing numerical errors of iterative PDE solvers and compare different learning approaches for finding complex correction functions. We find that previously used learning approaches are… Expand
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References

SHOWING 1-10 OF 88 REFERENCES
Learning Neural PDE Solvers with Convergence Guarantees
Learning data-driven discretizations for partial differential equations
Learning to Control PDEs with Differentiable Physics
Accelerating Eulerian Fluid Simulation With Convolutional Networks
PDE-Net: Learning PDEs from Data
DGM: A deep learning algorithm for solving partial differential equations
Learning to Simulate Complex Physics with Graph Networks
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