ROBUST AND SCALABLE MULTI-FIDELITY ALGORITHMS FOR MODEL-BASED PREDICTIONS
Abstract
Modern computational models are complex in nature: accurate predictions of physics require detailed and intensive computational resources. As such, development of accurate scientific models has been the area of research emphasis in recent decades. Today’s scientific models involve largescale simulation tools, often with many interdependent components, and sometimes requiring days to complete a single simulation. Adding to this complexity is the presence of uncertainty, which is often encoded into models via parameters or random variables. Any direct approach to analyze the impact of parametric variation on such expensive models is infeasible. One approach to circumvent this limitation is to utilize hierarchies of models, each with differing computational costs and predictive fidelities. Research in the past few years has demonstrated that intelligent allocation of resources across this ensemble of models can produce predictions with much greater accuracy than concentrating all resources in a single model. Such multi-fidelity procedures hold the potential to optimally utilize ensembles of models to make predictions. The main components of this proposed project address optimal resource allocation and robust and scalable model reduction, generation, and learning via low-rank multi-fidelity and multilevel procedures.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 12, 2021
- Source ID
- FA95502010138
Entities
People
- Alireza Doostan
Organizations
- Air Force Office of Scientific Research
- Regents of the University of Colorado
- United States Air Force