ROBUST AND SCALABLE MULTI-FIDELITY ALGORITHMS FOR MODEL-BASED PREDICTIONS
Abstract
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. The overall goal is the construction of surrogate models with accuracy guarantees that can be used in design optimization, inference, and general uncertainty quantification scenarios. The tasks associated with this project involve fundamental mathematical and algorithmic advances in low-rank multi-fidelity methods. Error certificates to ensure accuracy will be developed when possible. Kernel learning techniques will be employed to explore problem-dependent low-rank structure and optimize allocation of resources. Algorithmic methods to handle heterogeneous models, data, and parameter spaces will be developed resulting in a comprehensive framework for utilizing low-rank multi-fidelity methods.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Aug 12, 2021
- Source ID
- FA95502010338
Entities
People
- Akil C. Narayan
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Utah