Learned discretizations for passive scalar advection in a two-dimensional turbulent flow
Physical Review Fluids2021Vol. 6(6)
Citations Over TimeTop 10% of 2021 papers
Abstract
Calculating the evolution of a passive scalar in a turbulent flow requires resolving the intricate stretching and folding of the scalar field. Traditionally, this requires that the computational mesh is much smaller than the smallest scale of the concentration field. Here we demonstrate the use of machine learning to learn discretizations of the governing equation that give accurate computations with a coarser mesh. The model learns the universal small scale structures of the concentration field stretching, allowing it to accurately interpolate with less information.
Related Papers
- → GPU acceleration of an unmodified parallel finite element Navier-Stokes solver(2009)60 cited
- → HIPERCONE FDTD: Vectorized highly scalable full-wave electromagnetic solver(2018)1 cited
- → Contact Points Discovery for Soft-Body Manipulations with Differentiable Physics(2022)4 cited
- → Development of a Chemically Reacting Flow Solver on the Graphic Processing Units(2011)
- → Creating and Using Solvers in the Openfoam Package for Modeling the Temperature Field(2023)