2 papers
physics.flu-dyn2026
Data-driven modeling of multiscale phenomena with applications to fluid turbulence
Brandon Choi, Matteo Ugliotti, Mateo Reynoso +2
This paper introduces a novel data driven framework for constructing accurate and general equivariant models of multiscale phenomena which does not rely on specific assumptions abo…
physics.flu-dyn2026
Physics-informed data-driven inference of an interpretable equivariant LES model of incompressible fluid turbulence
Matteo Ugliotti, Brandon Choi, Mateo Reynoso +2
Restrictive phenomenological assumptions represent a major roadblock for the development of accurate subgrid-scale models of fluid turbulence. Specifically, these assumptions limit…