This $383,840 Project Grant, awarded by the National Science Foundation (NSF) Division of Mathematical Sciences under the Mathematical and Physical Sciences (CFDA 47.049) program, aims to advance reliable, data-driven optimization methods for complex physics-based simulation models. The project will focus on enhancing the design of renewable tidal energy farms by analyzing the computational resources required for optimizing these challenging infinite-dimensional optimization problems. Key objectives include quantifying the accuracy and reliability of low-fidelity and sample-based approximations of deterministic, risk-averse, and chance-constrained optimization problems with nonsmooth, nonconvex functions. The research will employ high-dimensional statistics and nonsmooth analysis to understand the generalization properties of sample-based and data-driven solutions. Open-source software and simulation outputs will be created and disseminated through publications and student engagement activities. This 3-year project is led by the Georgia Tech Research Corporation, the non-profit research arm of the Georgia Institute of Technology.
Generated 3/4/25, 6:36 AM