Development of a Dynamic Data-Driven Uncertainty Quantification Systems
Abstract
The main focus of this work is the development of a new Polynomial Chaos based DynamicData-Driven Uncertainty Quantification system (DDDUQ). Uncertainty Quantification (UQ)is important for many science and engineering applications. The challenge that this work seeksto address is the long-term integration problem, where simulations are used to forecast physicsover long temporal and/or spatial extrapolation intervals. This work provides a novel approachfor guiding both the measurement and simulation processes towards improving extrapolationaccuracies and UQ. This new approach is applied to satellite conjunction assessments whichrequires UQ for accurate probability calculations.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Apr 09, 2018
- Source ID
- FA95501810149
Entities
People
- Richard Linares
Organizations
- Air Force Office of Scientific Research
- Regents of the University of Minnesota
- United States Air Force