Work

Data-driven reduced-order models for port-Hamiltonian systems with operator inference

Year: 2025

Type: article

Source: Computer Methods in Applied Mechanics and Engineering

DOI: 10.1016/j.cma.2025.118042

Authors: Yuwei Geng, Lili Ju, Boris Kramer, Zhu Wang

Institutions: University of South Carolina, University of California San Diego

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, U.S. Department of Energy, University of South Carolina, Scheme for Promotion of Academic and Research Collaboration, Office of Naval Research, Office of the Vice President for Research and Partnerships

Awards: G1765713809, FRG: Collaborative Research: Variationally Stable Neural Networks for Simulation, Learning, and Experimental Design of Complex Physical Systems, G2330613517, G2966579964, G2968171088, G4720003262, State and Parameter Estimation: Variationally Stable Models and Physics-Informed Learning, RELIABLE AND EFFICIENT MACHINE LEARNING FOR LEADERSHIP FACILITY SCIENTIFIC DATA ANALYTICS, G7427201681, G8876996369, RTG: Mathematical Foundation of Data Science at University of South Carolina

OpenAlex ID: W4410138181

FWCI: 2.143

Cites: 53

Cited by: 5

Related to: 10