Discrete Physics-Informed Training for Projection-Based Reduced-Order Models with Neural Networks

Discrete Physics-Informed Training for Projection-Based Reduced-Order Models with Neural Networks

This paper presents a physics-informed training framework for projection-based Reduced-Order Models (ROMs). We extend the original PROM-ANN architecture by complementing snapshot-based training with a FEM-based, discrete physics-informed residual loss, bridging the gap between traditional projection...

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Bibliographic Details
Main Authors: Nicolas Sibuet, Sebastian Ares de Parga, Jose Raul Bravo, Riccardo Rossi
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Axioms
Subjects:
reduced-order model (ROM)
physics-informed neural networks (PINNs)
artificial neural network (ANN)
projection-based model reduction
proper orthogonal decomposition (POD)
Online Access:https://www.mdpi.com/2075-1680/14/5/385
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