Title: Extreme‐Scale Stochastic Optimization and Simulation via Learning‐Enhanced Decomposition and Parallelization
Funding Source: Department of Energy Early Career Research Program, Office of Advanced Scientific Computing Research
The purpose of this research is to incorporate machine learning techniques into decomposition algorithms for solving stochastic optimization and simulation models using high performance computing. We consider a broad class of complex decision‐making problems, where discrete or continuous decisions are made before and/or after knowing multiple and potentially correlated sources of uncertainties. Examples include Cloud Computing service scheduling, sensor deployment for monitoring critical infrastructures, and other resource allocation and operational problems in energy and national security.
Please see https://science.energy.gov/early-career/ for the official announcement.
