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5c8df54e4a5f26a28581 · A SUPER-METAMODELLING FRAMEWORK TO OPTIMIZE SYSTEM PREDICTABILITY

A SUPER-METAMODELLING FRAMEWORK TO OPTIMIZE SYSTEM PREDICTABILITY

Statistical metamodels can robustly predict manufacturing process and engineering systems design results. Various techniques, such as Kriging, polynomial regression, artificial neural network and others, are each best suited for different scenarios that

Официальный источник · 2018-08-25

Полные импортированные сведения карточки
{
  "title_en": "A SUPER-METAMODELLING FRAMEWORK TO OPTIMIZE SYSTEM PREDICTABILITY",
  "designation": "",
  "series": "",
  "status": "",
  "date": "2018-08-25",
  "date_original": "August 25, 2018",
  "url": "https://www.nist.gov/publications/super-metamodelling-framework-optimize-system-predictability",
  "source": "NIST Publications",
  "display_en": "",
  "authors_en": "Yan Lu , Douglas Eddy, Sundar Krishnamurty, Ian Grosse",
  "abstract_excerpt_en": "Statistical metamodels can robustly predict manufacturing process and engineering systems design results. Various techniques, such as Kriging, polynomial regression, artificial neural network and others, are each best suited for different scenarios that",
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  "sources": [
    "NIST Publications"
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    {
      "title_en": "A SUPER-METAMODELLING FRAMEWORK TO OPTIMIZE SYSTEM PREDICTABILITY",
      "designation": "",
      "series": "",
      "status": "",
      "date": "2018-08-25",
      "date_original": "August 25, 2018",
      "url": "https://www.nist.gov/publications/super-metamodelling-framework-optimize-system-predictability",
      "source": "NIST Publications",
      "display_en": "",
      "authors_en": "Yan Lu , Douglas Eddy, Sundar Krishnamurty, Ian Grosse",
      "abstract_excerpt_en": "Statistical metamodels can robustly predict manufacturing process and engineering systems design results. Various techniques, such as Kriging, polynomial regression, artificial neural network and others, are each best suited for different scenarios that"
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