Work

A transferable PINN-based method for quantum graphs with unseen structure

Year: 2025

Type: article

Abstract: This study introduces a transferable approach for solving partial differential equations (PDEs) on metric graphs, often called quantum graphs, employing Physics-Informed Neural Networks (PINNs). Unlike traditional solvers constrained by specific graph structures, our method utilizes a Neumann-Neumann domain decomposition technique, offering adaptability across various network topologies. By incorporating edge-wise surrogates, this approach achieves experimental results comparable to those obtained with FEM across diverse network configurations.

Source: IFAC-PapersOnLine

DOI: 10.1016/j.ifacol.2025.03.013

Authors: Csongor L. Laczkó, Mihály A. Vághy, Mihály Kovács

Institutions: Pázmány Péter Catholic University, Budapest University of Technology and Economics, Chalmers University of Technology, University of Gothenburg

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: diamond

Funders: Ministry of Science and Technology, Israel, Nemzeti Kutatási Fejlesztési és Innovációs Hivatal, Nemzeti Kutatási, Fejlesztési és Innovaciós Alap

Awards: G4204975496, G9000145849