Software Open Access
Stiller, Patrick;
Zhdanov, Maksim;
Rustamov, Jeyhun;
Bethke, Friedrich;
Hoffmann, Nico
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<dct:title>Neural Solvers</dct:title>
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<dcat:keyword>PINNs</dcat:keyword>
<dcat:keyword>PDEs</dcat:keyword>
<dcat:keyword>Neural Solver</dcat:keyword>
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<dct:description><p>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.</p></dct:description>
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