TY - JOUR
T1 - Combining simulations and data with deep learning and uncertainty quantification for advanced energy modeling
AU - Radaideh, Majdi I.
AU - Kozlowski, Tomasz
N1 - Funding Information:
This work is supported by the U.S. Department of Energy under the awards (16-10908) and (DE-NE0008573), which are provided through the Nuclear Energy University Program (NEUP).
Publisher Copyright:
© 2019 John Wiley & Sons, Ltd.
PY - 2019/11/1
Y1 - 2019/11/1
N2 - 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.
AB - 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.
KW - data science
KW - deep learning
KW - modeling and simulation
KW - nuclear energy
KW - sensitivity analysis
KW - uncertainty quantification
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U2 - 10.1002/er.4698
DO - 10.1002/er.4698
M3 - Article
AN - SCOPUS:85070744319
SN - 0363-907X
VL - 43
SP - 7866
EP - 7890
JO - International Journal of Energy Research
JF - International Journal of Energy Research
IS - 14
ER -