<ul><li>This paper focuses on performing a complete uncertainty quantification analysis of a 2D slab burner direct numerical simulation (DNS).</li><li>The study addresses challenges related to developing data-driven surrogate models, propagating parametric uncertainties, and Bayesian calibration of latent heat and chemical reaction parameters.</li><li>Two surrogate models, Gaussian Process (GP) and Hierarchical Multiscale Surrogate (HMS), were constructed using ensemble simulations generated via Latin Hypercube sampling.</li><li>The study emphasizes the importance of surrogate model selection and parameter calibration in quantifying uncertainty in predictions of fuel regression rates in complex combustion systems.</li></ul>

UQ of 2D Slab Burner DNS: Surrogates, Uncertainty Propagation, and Parameter Calibration

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