Enhanced Lasso Regularization-Based Self-Adaptive Feature Selection Algorithm for the High-Dimensional Uncertainty Quantification of TREAT Transient Test Modeling-Reference-Cited by-同舟云学术

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Enhanced Lasso Regularization-Based Self-Adaptive Feature Selection Algorithm for the High-Dimensional Uncertainty Quantification of TREAT Transient Test Modeling

Published:2020-05-26 Issue:6 Volume:206 Page:839-861
ISSN:0029-5450
Container-title:Nuclear Technology
language:en
Short-container-title:Nuclear Technology

Author:

Zhou Haining¹,Seker Volkan¹,Downar Thomas¹^ORCID

Affiliation:

1. University of Michigan, Ann Arbor, Michigan

Funder

U.S. Department of Energy

Publisher

Informa UK Limited

Subject

Condensed Matter Physics,Nuclear Energy and Engineering,Nuclear and High Energy Physics

Link

https://www.tandfonline.com/doi/pdf/10.1080/00295450.2020.1746620

Reference49 articles.

1. B. BENJAMIN, O. JAVIER, and D. MARK, “FY 2017 Modeling of the M8 Calibration Series Using Mammoth,” Technical Report INL/EXT-42415, Idaho National Laboratory (2017).

2. TREAT M2 experiment modeling for transient benchmark analysis

3. Investigations of Rod Positions for Treat M8CAL Analyses

4. Interpretation of energy deposition data from historical operation of the transient test facility (TREAT)

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1. Uncertainty Quantification for Multiphase Computational Fluid Dynamics Closure Relations with a Physics-Informed Bayesian Approach;Nuclear Technology;2023-02-16

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