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Neural Solvers

Stiller, P.; Zhdanov, M.; Rustamov, J.; Bethke, F.; Hoffmann, N.

Neural Solvers are neural network-based solvers for partial differential equations and inverse problems. The framework implements scalable physics-informed neural networks Physics-informed neural networks allow strong scaling by design. Therefore, we have developed a framework that uses data parallelism to accelerate the training of physics-informed neural networks significantly. To implement data parallelism, we use the Horovod framework, which provides near-ideal speedup on multi-GPU regimes.

Keywords: PINNs; PDEs; Neural Solver; Scalable AI

  • Software in the HZDR data repository RODARE
    Publication date: 2021-09-06
    DOI: 10.14278/rodare.1193
    License: CC-BY-1.0

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Permalink: https://www.hzdr.de/publications/Publ-33172
Publ.-Id: 33172