CEE Seminar: Enhancing Computational Simulation with Physics- and Data-Informed Surrogate Models

Computational simulation has allowed scientists to explore, observe, and test physical regimes previously thought to be unattainable. Bayesian analysis provides a natural framework for incorporating the uncertainties that undeniably exist in computational modeling. However, the ability to perform quality Bayesian and uncertainty analyses is often limited by the computational expense of first-principles physics models. In the absence of a reliable low-fidelity physics model, phenomenological surrogate models can be used to mitigate this expense; however, phenomenological models may not adhere to known physics or properties. Furthermore, the interactions of complex physics in high-fidelity codes lead to dependencies between quantities of interest (QoIs) that are difficult to quantify and capture when individual surrogates are used for each observable. Predicting multiple QoIs with a single surrogate preserves valuable insights regarding the correlated behavior of the target observables and maximizes the information gained from available data. Applications to thermonuclear ignition and hypersonic reentry are discussed.