A Priori Uncertainty Quantification of Reacting Turbulence Closure Models using Bayesian Neural Networks
<|reference_start|>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 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 neural networks (BNNs) to capture both epistemic and aleatoric uncertainties in a reacting flow model. In particular, we model the filtered progress variable scalar dissipation rate which plays a key role in the dynamics of turbulent premixed flames. We demonstrate that BNN models can provide unique insights about the structure of uncertainty of the data-driven closure models. We also propose a method for the incorporation of out-of-distribution information in a BNN. The efficacy of the model is demonstrated by a priori evaluation on a dataset consisting of a variety of flame conditions and fuels.<|reference_end|>
@article{pash2024a,
    title={A Priori Uncertainty Quantification of Reacting Turbulence Closure
  Models using Bayesian Neural Networks},
    author={Graham Pash, Malik Hassanaly, Shashank Yellapantula},
    journal={arXiv preprint arXiv:2402.18729},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.18729},
    primaryClass={physics.flu-dyn cs.LG physics.data-an}
}