Published: Oct 8, 2024 by Daning Huang
We hosted Dr. Ashwin Renganathan from AERSP/ICDS of PSU for a CAV talk via the AI/ML technical group. Dr. Renganathan is an expert in Bayesian decision theory.
Abstract Modern day engineering decision-making involves one or more computer simulation oracles of an engineered system which can be queried on-demand to learn the system response to control input. Querying simulation oracles, also called “computer experiments”, incur a non-trivial computational cost, which increases with the level of fidelity in the underlying models. For instance, a realistic computational aerodynamic simulation of an aircraft can cost several thousands of CPU hours to compute—anything more than a few dozens of such simulations is prohibitive. Therefore, a central goal of engineering decision-making is to optimally design computer experiments, to maximize the value of information extracted at minimal computational effort.
In this talk, we will address problems anchored in the decision-making triad namely: experiment design, uncertainty quantification, and numerical optimization. Specifically, using variants of Gaussian process surrogate models and a Bayesian decision theoretic framework, we will show that problems in the decision-making triad can be solved in a principled, theoretically sound and, yet, (computational) cost-effective manner. We will show demonstrations on applications in computational aerodynamics.