Séminaire du CERAMATHS - DMATHS : exposé d'Iraj Mortazavi

Le séminaire du département de mathématiques du CÉRAMATHS accueillera à 14h Iraj Mortazavi (CNAM, Paris), jeudi 12 février 2026, pour l'exposé suivant :

Machine Learning to Accelerate Fluid-Structure Interaction Simulations

In this work, we propose a non-intrusive data-driven predictor that couples reduced-order models of both the solid and fluid subproblems, providing an initial guess for the nonlinear problem of the next time step calculation. Each reduced order model is composed of a nonlinear encoder-regressor-decoder architecture and is equipped with an adaptive update strategy that adds robustness for extrapolation. In doing so, the proposed methodology leverages physics-based insights from high-fidelity solvers, thus establishing a physics-aware machine learning predictor.

With two strongly coupled FSI examples, this study demonstrates the improved convergence obtained with the new predictor and the overall numerical speedup realized compared to classical approaches.