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