Physics-based linear regression for high-dimensional forward uncertainty quantification

被引：0
|
作者：
Wang, Ziqi [1 ]
机构：
[1] Univ Calif Berkeley, Dept Civil & Environm Engn, Berkeley, CA 94720 USA
来源：
JOURNAL OF COMPUTATIONAL PHYSICS | 2025年 / 523卷
关键词：
High-dimensional regression; Physics-based surrogate modeling; Uncertainty quantification;
D O I：
10.1016/j.jcp.2024.113668
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
We introduce linear regression using physics-based basis functions optimized through the geometry of an inner product space. This method addresses the challenge of surrogate modeling with high-dimensional input, as the physics-based basis functions encode problem-specific information. We demonstrate the method using two proof-of-concept stochastic dynamic examples.
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页数：5
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