Dimensional Reduction of Highly Nonlinear Multiscale Models Using Most Appropriate Local Reduced-Order Bases
Abstract
The potential of simulation based engineering science for providing a deeper understanding of complex engineering systems, improving design reliability, reducing design-cycle time, and enhancing their performance is well recognized today in many fields. Yet, in many applications, high-fidelity simulations remain so computationally intensive that they cannot be performed as often as needed. Consequently, their impact has not been as strong for routine analysis,parametric studies, and time-critical applications, which demand a game-changing computational technology that leverages high performance computing with low-dimensional computational models to perform in real-time. Nonlinear, Projection-based Model Order Reduction (PMOR) can provide this leverage.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- May 02, 2017
- Source ID
- FA95501710182
Entities
People
- Charbel Farhat
Organizations
- Air Force Office of Scientific Research
- Stanford University
- United States Air Force