Tensor Approaches for Simulating Kinetic Systems
Abstract
We propose a comprehensive collaborative ef fort to tackle the wide-ranging theoretical and computational challenges associated with approximating time-dependent high-dimensional PDEs on tensor manifolds. The core of our proposal centers around advancing tensor- based algorithms that uphold the intrinsic structures within a reduced low-rank representation of dynamics (Thrust 1), as well as to push the computing capabilities of extremely high-dimensional equations via data-driven learning approaches for tensor representations and model reduction (Thrust 2). The efficiency and robustness of tensor-based algorithms developed in Thrusts 1 and 2 hinges on the comprehensive development of tensor decomposition and optimization algorithms (Thrust 3).
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Feb 06, 2025
- Source ID
- FA95502410254
Entities
People
- Jingmei Qiu
Organizations
- Air Force Office of Scientific Research
- United States Air Force
- University of Delaware