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Financement de l’UE (175 920 €) : Techniques systématiques pour l’inférence robuste et les fermetures explicables basées sur les données pour la physique des plasmas Hor16/04/2024 Programme de recherche et d'innovation de l'UE « Horizon »
Texte
Techniques systématiques pour l’inférence robuste et les fermetures explicables basées sur les données pour la physique des plasmas
Plasma physics has seen a long tradition of deriving simplified models such as magnetohydrodynamics through a process known as closure: starting from computationally demanding kinetics and following various analytical approximate schemes resulting in fluid-type models which are less accurate but more tractable. Due to smaller computational footprint these models have found applications in space weather modelling and fusion. The problem is that in many interesting applications, e.g. where collisions between particles are rare, the analytic closures have limitations. The goal of STRIDE (Systematic Techniques for Robust Inference and Data-driven Explainable closures for plasma) is to use machine learning to construct models with fewer degrees of freedom that describe kinetic processes relevant for Geospace Environmental Modelling (GEM), such as magnetic reconnection. Corrections to fluid-type models will be learned with deep neural networks and equation discovery and tested in numerical simulations. The important challenge involves understanding how such surrogates can be made robust against out-of-distribution shifts (i.e. different physical conditions) and numerical instabilities. Thus the closures will be first trained on data generated by high fidelity physics-based model, e.g. kinetic Particle-in-Cell simulations, for a specific set of parameters and then transfer learning will be applied to a different set of parameters to improve robustness. Uncertainty quantification and the ability to generate extremes will be investigated. The scientific question to be addressed here include: how can we interpret the trained models using explainable AI, what are the optimal ways of performing transfer learning and fine-tuning on the observational data, are there physical considerations (such as conservation laws or symmetries) that can reduce the costs associated with training such machine learning models? Trained models will be made open-source to foster research.
| Katholieke Universiteit Leuven | 175 920 € |
https://cordis.europa.eu/project/id/101148539
Cette annonce se réfère à une date antérieure et ne reflète pas nécessairement l’état actuel. L’état actuel est présenté à la page suivante : Katholieke Universiteit Leuven privaatrechtelijke rechtspersoon, Leuven, Belgique.
