Work

An AI-driven reduced order model for edge tokamak turbulence

Year: 2025

Type: article

Abstract: We present an AI-driven reduced order model (ROM) for drift-wave turbulence dynamics based on proper orthogonal decomposition (POD) combined with Galerkin projection. Using the Hasegawa–Wakatani model as a proof of concept, our framework integrates neural ordinary differential equations (NODEs) with POD. In this formulation, a neural network learns the time derivative of the reduced order state, effectively modeling the underlying vector field that governs the system's dynamics. This allows direct access to the phase portrait of the system and provides insights into trends and patterns by capturing its continuous temporal evolution. Results show that the proposed method reconstructs key features of the system and holds promise for significant computational speed-up.

Source: Physics of Plasmas

DOI: 10.1063/5.0272986

Authors: David Garrido González, Nathaniel Saura, Peter Beyer, Waleed Mouhali, Sadruddin Benkadda

Institutions: Centre National de la Recherche Scientifique, Aix-Marseille Université, Physique des interactions ioniques et moléculaires, École Centrale d'Électronique

Language: English

Topic: Model Reduction and Neural Networks

Subfield: Statistical and Nonlinear Physics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: hybrid

Funders: EUROfusion, European Commission, Euratom Research and Training Programme

Awards: Implementation of activities described in the Roadmap to Fusion during Horizon 2020 through a Joint programme of the members of the EUROfusion consortium, G829439530, G858011915