Year: 2024
Type: preprint
Abstract: 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 a proof-of-concept nonlinear random vibration example.
Source: arXiv (Cornell University)
Author: Wang, Ziqi
Institution: University of California, Berkeley
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4396945185