September 6, 2021 Software Open Access

Stiller, Patrick; Zhdanov, Maksim; Rustamov, Jeyhun; Bethke, Friedrich; Hoffmann, Nico

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  <dc:creator>Stiller, Patrick</dc:creator>
  <dc:creator>Zhdanov, Maksim</dc:creator>
  <dc:creator>Rustamov, Jeyhun</dc:creator>
  <dc:creator>Bethke, Friedrich</dc:creator>
  <dc:creator>Hoffmann, Nico</dc:creator>
  <dc:date>2021-09-06</dc:date>
  <dc:description>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.</dc:description>
  <dc:identifier>https://rodare.hzdr.de/record/1194</dc:identifier>
  <dc:identifier>10.14278/rodare.1194</dc:identifier>
  <dc:identifier>oai:rodare.hzdr.de:1194</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>url:https://arxiv.org/pdf/2009.03730.pdf</dc:relation>
  <dc:relation>url:https://www.hzdr.de/publications/Publ-33172</dc:relation>
  <dc:relation>doi:10.14278/rodare.1193</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/matter</dc:relation>
  <dc:relation>url:https://rodare.hzdr.de/communities/rodare</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/1.0/legalcode</dc:rights>
  <dc:subject>PINNs</dc:subject>
  <dc:subject>PDEs</dc:subject>
  <dc:subject>Neural Solver</dc:subject>
  <dc:subject>Scalable AI</dc:subject>
  <dc:title>Neural Solvers</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>software</dc:type>
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