Software Open Access

Neural Solvers

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


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  "keywords": [
    "PINNs", 
    "PDEs", 
    "Neural Solver", 
    "Scalable AI"
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  "inLanguage": {
    "@type": "Language", 
    "name": "English", 
    "alternateName": "eng"
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  "datePublished": "2021-09-06", 
  "license": "https://creativecommons.org/licenses/by/1.0/legalcode", 
  "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>", 
  "contributor": [], 
  "name": "Neural Solvers", 
  "@type": "SoftwareSourceCode", 
  "url": "https://rodare.hzdr.de/record/1194", 
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  "@id": "https://doi.org/10.14278/rodare.1194", 
  "creator": [
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      "@type": "Person", 
      "name": "Stiller, Patrick", 
      "@id": "https://orcid.org/0000-0003-1950-069X"
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      "name": "Zhdanov, Maksim"
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      "name": "Rustamov, Jeyhun"
    }, 
    {
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      "name": "Bethke, Friedrich"
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    {
      "@type": "Person", 
      "name": "Hoffmann, Nico"
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  "identifier": "https://doi.org/10.14278/rodare.1194", 
  "version": "0.1"
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