metamodel calibration

{
  "id": "MLC-T-ENV-017079",
  "term": "metamodel calibration",
  "field": "Environment",
  "definition": "1. The practice of determining unknown parameters of a model by the following steps: 1) Run a computer experiment by varying the unknown parameters, and recording the expected responses. 2) Fit a model of general form, especially a neural network, using the responses of the computer experiment as inputs and the factors as the outputs. 3) Extract the unknown parameters as the outputs that result from this model when the inputs are taken to be the empirically observed values.\n\n2. The practice of determining unknown parameters of a model by the following steps: 1) Run a computer experiment by varying the unknown parameters, and recording the expected responses. 2) Fit a model of general form, especially a neural network, using the responses of the computer experiment as inputs and the factors as the outputs. 3) Extract the unknown parameters as the outputs that result from this model when the inputs are taken to be the empirically observed values. [NIST/SEMATECH 2005b]",
  "subject": "Health and safety",
  "references": [
    "NIST/SEMATECH 2005b",
    "Thesaurus of Terms Used in Microbial Risk Assessment"
  ],
  "url": "https://mlchart.com/terminology/environment/metamodel-calibration/"
}