Inference-oriented model reduction- A new paradigm
Abstract
Inverse problems seek to characterize an underlying state or parameter of a system from indirectly observed data. Such problems are ubiquitous in science and engineering with applications including medical imaging, weather prediction, real-time flow estimation for flow control, and more. Solving inverse problems numerically is often extremely expensive because typical algorithms require the repeated evaluation of a high-dimensional computational model of the underlying physical system (one evaluation per sample or optimization iteration in typical inference algorithms). To reduce the cost of inference algorithms, efficient surrogate models for the underlying system are needed. The objective of this project is to develop a new paradigm for inference- oriented projection- based model reduction which exploits the known low-dimensional intrinsic dimensionality of inverse problems.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 06, 2025
- Source ID
- FA95502410105
Entities
People
- Elizabeth Qian
Organizations
- Air Force Office of Scientific Research
- United States Air Force