A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks

arXiv:2402.18729v1 Announce Type: cross
Abstract: While many physics-based closure model forms have been posited for the sub-filter scale (SFS) in large eddy simulation (LES), vast amounts of data available from direct numerical simulation (DNS) create opportunities to leverage data-driven modeling techniques. Albeit flexible, data-driven models still depend on the dataset and the functional form of the model chosen. Increased adoption of such models requires reliable uncertainty estimates both in the data-informed and out-of-distribution regimes. In this work, we employ Bayesian …

abstract arxiv bayesian cs.lg data data-driven dns filter forms modeling networks neural networks numerical opportunities physics physics.data-an physics.flu-dyn quantification scale simulation turbulence type uncertainty vast