Work

Plane-wave decomposition and randomised training; a novel path to generalised physics-informed neural networks for simple harmonic motion

Year: 2025

Type: article

Abstract: In this paper, we introduce a formulation of physics-informed neural networks (PINNs), based on learning the form of the Fourier decomposition, and a training methodology based on a spread of randomly chosen boundary conditions. By training in this way we produce a PINN that generalises; after training it can be used to correctly predict the solution for an arbitrary set of boundary conditions and interpolate this solution between the samples that spanned the training domain. We demonstrate for a toy system of two coupled oscillators that this gives the PINN formulation genuine predictive capability owing to an effective reduction of the training to evaluation times ratio resulting from this decoupling of the solution from specific boundary conditions.

Source: Machine Learning Science and Technology

DOI: 10.1088/2632-2153/ae143b

Authors: Rory Clements, James Ellis, Geoff Hassall, Simon Horsley, Gavin Tabor

Institutions: University of Exeter, Oxford Instruments (United Kingdom)

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: $3,000

Funder: Engineering and Physical Sciences Research Council