Combining simulations and data with deep learning and uncertainty quantification for advanced energy modeling

Majdi I. Radaideh, Tomasz Kozlowski

Research output: Contribution to journalArticlepeer-review


A novel and modern framework for energy modeling is developed in this paper with a focus on nuclear energy modeling and simulation. The framework combines multiphysics simulations and real data, with validation by uncertainty quantification tasks and facilitation by machine and deep learning methods. The hybrid framework is built on the basis of a wide range of physical models, real data, mathematical and statistical methods, and artificial intelligence techniques. The framework is demonstrated in different applications, including quantifying uncertainties in computer simulations, multiphysics coupling, analysis of variance using machine learning surrogate models, deep learning of time series phenomena, and propagating parametric uncertainties of nuclear data. The applications demonstrated are oriented to nuclear engineering simulations, even though majority of the methods are applicable to other energy sources (eg, renewable). Efficient utilization of this framework is expected to yield a much better understanding of the physical phenomena analyzed as well as an improvement in the performance of the energy design/model under construction.

Original languageEnglish (US)
Pages (from-to)7866-7890
Number of pages25
JournalInternational Journal of Energy Research
Issue number14
StatePublished - Nov 1 2019
Externally publishedYes


  • data science
  • deep learning
  • modeling and simulation
  • nuclear energy
  • sensitivity analysis
  • uncertainty quantification

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Nuclear Energy and Engineering
  • Fuel Technology
  • Energy Engineering and Power Technology


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