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The STRIDE project aims to enhance plasma physics modeling by applying machine learning techniques to improve the accuracy of fluid-type models in geospace environmental applications. It focuses on developing robust models that can handle varying physical conditions and uncertainties.
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 collis…
KATHOLIEKE UNIVERSITEIT LEUVEN
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