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Acceleration of a Physics-Based Machine Learning Approach for Modeling and Quantifying Model-Form Uncertainties and Performing Model Updating

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Article number011009
JournalJournal of Computing and Information Science in Engineering
Volume23
Issue number1
DOIs
StatePublished - Feb 2023
Externally publishedYes

ASJC Scopus Subject Areas

  • Software
  • Computer Science Applications
  • Computer Graphics and Computer-Aided Design
  • Industrial and Manufacturing Engineering

Keywords

  • data-driven engineering
  • inverse methods for engineering applications
  • machine learning for engineering applications
  • physics-based simulations
  • qualification
  • verification and validation of computational models

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