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Supplementary Data and Code for "Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks"

Venzke, Andreas; Qu, Guannan; Low, Steven; Chatzivasileiadis, Spyros


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{
  "inLanguage": {
    "alternateName": "eng", 
    "@type": "Language", 
    "name": "English"
  }, 
  "description": "<p>Online appendix containing supplementary data and code to reproduce the simulation results in A. Venzke, G. Qu, S. Low and S. Chatzivasileiadis, &quot;Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks&quot;.</p>\n\n<p>See readme.txt for a detailed description.</p>\n\n<p>When publishing results based on this data/code, please cite: A. Venzke, G. Qu, S. Low and S. Chatzivasileiadis, &quot;Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks&quot;, 2020. Available online: https://arxiv.org/abs/2006.11029</p>", 
  "license": "https://creativecommons.org/licenses/by/4.0/legalcode", 
  "creator": [
    {
      "affiliation": "Technical University of Denmark", 
      "@id": "https://orcid.org/0000-0002-6101-6001", 
      "@type": "Person", 
      "name": "Venzke, Andreas"
    }, 
    {
      "affiliation": "California Institute of Technology", 
      "@type": "Person", 
      "name": "Qu, Guannan"
    }, 
    {
      "affiliation": "California Institute of Technology", 
      "@type": "Person", 
      "name": "Low, Steven"
    }, 
    {
      "affiliation": "Technical University of Denmark", 
      "@id": "https://orcid.org/0000-0003-4698-8694", 
      "@type": "Person", 
      "name": "Chatzivasileiadis, Spyros"
    }
  ], 
  "url": "https://zenodo.org/record/3871755", 
  "datePublished": "2020-06-19", 
  "version": "V1.0", 
  "keywords": [
    "Neural networks", 
    "Mixed-integer linear programming", 
    "Optimal power flow"
  ], 
  "@context": "https://schema.org/", 
  "identifier": "https://doi.org/10.5281/zenodo.3871755", 
  "@id": "https://doi.org/10.5281/zenodo.3871755", 
  "@type": "SoftwareSourceCode", 
  "name": "Supplementary Data and Code for \"Learning Optimal Power Flow: Worst-Case Guarantees for Neural Networks\""
}
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