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Center for Nonlinear Studies |
Relativistic fluid dynamics plays an increasingly important role in several fields of modern physics, finding application both at large scales, in the realm of astrophysics, down to atomic scales (e.g., in the study of the electron properties of graphene in effective 2D systems) and further down to subnuclear scales, in the realm of quark-gluon plasmas. This motivates the quest for powerful and efficient computational methods, able to accurately study fluid dynamics in the relativistic regime and possibly also to seamlessly bridge the gap between relativistic and low-speed non-relativistic fluid regimes.In this talk, I will provide an introduction to a novel lattice kinetic scheme which extends the applicability of the Lattice Boltzmann Method (LBM) to a wider range of kinetic parameters, allowing for the simulation of relativistic gases of massive particles in rarefied conditions. The numerical scheme builds on high order quadrature rules which allow to control the tread-off between accuracy and computational costs depending on the simulation parameters.Moreover, I will discuss a promising alternative path to high-order LBM, represented by combining LBM with Physics Informed Machine Learning, also presenting preliminary results in which a neural network is successfully trained as a surrogate of the single relaxation time BGK operator.
Host: Vitaliy Gyrya (T-5) and Daniel Livescu (CCS-2)