129 citations · 228 across the 8 of their papers we have counts for
3 papers · 1 filter
Physics-informed data-driven inference of an interpretable equivariant LES model of two-dimensional incompressible fluid turbulence
Matteo Ugliotti, Brandon Choi, Mateo Reynoso +2
Restrictive phenomenological assumptions limit the accuracy and generality of subgrid-scale models of fluid turbulence, especially in flows where coherent structures and bidirectio…
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…
Learning fluid physics from highly turbulent data using sparse physics-informed discovery of empirical relations (SPIDER)
Daniel R. Gurevich, Matthew R. Golden, Patrick A. K. Reinbold +1
We show how a complete mathematical description of a complicated physical phenomenon can be learned from observational data via a hybrid approach combining three simple and general…