Software Open Access

Neural Solvers

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


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{
  "id": "1194", 
  "language": "eng", 
  "title": "Neural Solvers", 
  "issued": {
    "date-parts": [
      [
        2021, 
        9, 
        6
      ]
    ]
  }, 
  "DOI": "10.14278/rodare.1194", 
  "publisher": "Rodare", 
  "version": "0.1", 
  "author": [
    {
      "family": "Stiller, Patrick"
    }, 
    {
      "family": "Zhdanov, Maksim"
    }, 
    {
      "family": "Rustamov, Jeyhun"
    }, 
    {
      "family": "Bethke, Friedrich"
    }, 
    {
      "family": "Hoffmann, Nico"
    }
  ], 
  "abstract": "<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>", 
  "type": "article"
}
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