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.

引用

页数：5

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