Combining simulations and data with deep learning and uncertainty quantification for advanced energy modeling-Reference-Cited by-同舟云学术

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

Published:2019-08-16 Issue:14 Volume:43 Page:7866-7890
ISSN:0363-907X
Container-title:International Journal of Energy Research
language:en
Short-container-title:Int J Energy Res

Author:

Radaideh Majdi I.¹^ORCID,Kozlowski Tomasz¹

Affiliation:

1. Department of Nuclear, Plasma, and Radiological Engineering University of Illinois at Urbana Champaign Urbana Illinois

Funder

U.S. Department of Energy

Publisher

Wiley

Subject

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

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/er.4698

Reference96 articles.

1. ENDF/B-VII.1 Nuclear Data for Science and Technology: Cross Sections, Covariances, Fission Product Yields and Decay Data

2. Modern Nuclear Data Evaluation with the TALYS Code System

3. Efficient Subspace Methods-Based Algorithms for Performing Sensitivity, Uncertainty, and Adaptive Simulation of Large-Scale Computational Models

4. Dimensionality reducibility for multi-physics reduced order modeling

5. Hybrid reduced order modeling applied to nonlinear models

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