Work

SINDy-BVP: Sparse identification of nonlinear dynamics for boundary value problems

Year: 2021

Type: article

Abstract: This article develops a data-driven model discovery and system identification technique for spatially-dependent boundary value problems to infer both the operator and spatially-dependent parameters that describe the heterogenous, physical quantities of a system.

Source: Physical Review Research

DOI: 10.1103/physrevresearch.3.023255

Authors: Daniel E. Shea, Steven L. Brunton, J. Nathan Kutz

Institution: University of Washington

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,625