New frontiers in theory, learning, and optimization for model reduction
Abstract
Development of accurate scientific models has been an area of recent research emphasis resulting in trusted full-order models (FOMs). These FOMs can be so large and complex that running simulations and-or analyzing scenario variability or parametric uncertainty is quite onerous. Direct approaches to analyze such parametric models is typically infeasible. Reduced order models (ROMs), which are computational approximations of FOMs, are proven methods that address challenges with FOMs. The main components of this project develop novel theory and algorithms for model reduction in parametric time-dependent model contexts, in particular for uncertainty quantification and inference. Effort will be focused where ROM applicability is currently challenging, such as parametric and transport-dominated problems. Applications of interest to AFOSR, such as PDE simulations related to kinetics and fluid flow, will be testbeds for the newly developed methods.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Mar 14, 2024
- Source ID
- FA95502310749
Entities
People
- Akil C. Narayan
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Utah