A forecasting metric for predictive modeling

Sez Atamturktur, Franois Hemez, Brian Williams, Carlos Tome, Cetin Unal

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


In science and engineering, simulation models calibrated against a limited number of experiments are commonly used to forecast at settings where experiments are unavailable, raising concerns about the unknown forecasting errors. Forecasting errors can be quantified and controlled by deploying statistical inference procedures, combined with an experimental campaign to improve the fidelity of a simulation model that is developed based on sound physics or engineering principles. This manuscript illustrates that the number of experiments required to reduce the forecasting errors to desired levels can be determined by focusing on the proposed forecasting metric.

Original languageEnglish (US)
Pages (from-to)2377-2387
Number of pages11
JournalComputers and Structures
Issue number23-24
StatePublished - Dec 2011

All Science Journal Classification (ASJC) codes

  • Civil and Structural Engineering
  • Modeling and Simulation
  • Materials Science(all)
  • Mechanical Engineering
  • Computer Science Applications


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