3 papers
physics.flu-dyn2026
Training of particle-turbulence sub-grid-scale closures with just particle data
G. Saltar Rivera, L. Villafane, J. B. Freund
If sufficient training data are available, neural networks are attractive for representing missing physics in simulations, such as sub-grid scales in the coarse-mesh particle-turbu…
cs.MS2025
MIRGE: An Array-Based Computational Framework for Scientific Computing
Matthias Diener, Matthew J. Smith, Michael T. Campbell +6
MIRGE is a computational approach for scientific computing based on NumPy-like array computation, but using lazy evaluation to recast computation as data-flow graphs, where nodes r…
cs.LG2024
A TVD neural network closure and application to turbulent combustion
Seung Won Suh, Jonathan F MacArt, Luke N Olson +1
Trained neural networks (NN) have attractive features for closing governing equations. There are many methods that are showing promise, but all can fail in cases when small errors…