Learning solution of a bond-based linear peridynamic model using LS-SVR method

Jie Ma, Zhiwei Yang and Ning Du

Mathematics and Computers in Simulation (MATCOM), 2024, vol. 217, issue C, 262-272

Abstract: In this paper, we develop an efficient least squares support vector regression (LS-SVR) method for a steady-state bond-based linear Peridynamic (PD) model in two space dimensions. To minimize a residual function associated with PD model, we introduce some dual variables to rewrite the optimization problem to a linear system and obtain a closed form approximate solution of the considered problem. The method is suitable to solve PD problem involving singular kernel, irregular geometrical domains. Numerical experiments are provided to show the accuracy and efficiency of the proposed method.

Keywords: Least squares support vector regression; Bond-based linear PD model; Closed form approximate solution; Irregular domain (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378475423004524
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:217:y:2024:i:c:p:262-272

DOI: 10.1016/j.matcom.2023.10.016

Access Statistics for this article

Mathematics and Computers in Simulation (MATCOM) is currently edited by Robert Beauwens

More articles in Mathematics and Computers in Simulation (MATCOM) from Elsevier
Bibliographic data for series maintained by Catherine Liu ().