Calibration using DAKOTA

Uses DAKOTA to perform deterministic and Bayesian calibration

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Version 1.1 - published on 22 Mar 2019

doi:10.21981/4Q55-FT75 cite this

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Abstract

This tool uses the DAKOTA software package to perform deterministic and Bayesian calibration. The default example is a calibration of a Sutton-Chen EAM potential, given a 'ground truth' dataset. Users can upload their own dataset and modify the Jupyter notebook to run a calibration of interest.

Cite this work

Researchers should cite this work as follows:

  • Saaketh Desai, Alejandro Strachan (2019), "Calibration using DAKOTA," https://nanohub.org/resources/dakotacalibrate. (DOI: 10.21981/4Q55-FT75).

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Tags

  1. Bayesian
  2. Bayesian calibration
  3. Calibration
  4. DAKOTA
  5. Jupyter notebooks