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

Haining Zhou
Haining Zhou

NUCLEAR TECHNOLOGY, pp. 839-861, 2020.

Cited by: 0|Views0

Abstract:

The paper presents a self-adaptive feature selection algorithm we developed for solving high-dimensional uncertainty quantification problems. The development of the algorithm was motivated and supported by the benchmarking of the Transient Reactor Test (TREAT) transient test 2857. The generalized polynomial chaos expansion scheme was adop...More

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