Low-rank approximation

Adaptive Local Proper Orthogonal Decomposition via Graph Partitioning and Provable Error-Bounded Lossy Compression

Roberto Nuca, Postdoctoral Research Fellow, Applied Mathematics and Computational Science

Nov 17, 15:00 - 16:00

B1 R3426

method of snapshots lossy compression proper orthogonal decomposition singular value decomposition adaptive spatial partitioning model order reduction Low-rank approximation

This talk introduces a spatially adaptive POD-based compression method combining hierarchical domain partitioning and local low-rank approximations for error-controlled, storage-efficient compression of spatially heterogeneous simulation data.

Darcy flow in random porous medium

AMCS 301 Numerical methods for random partial differential equations: hierarchical approximation and machine learning approaches

Random PDEs stochastic algorithms Monte carlo methods Quasi-Monte Carlo Hierarchical regression Multilevel Monte Carlo Stochastic collocation Multi-index Low-rank approximation hierarchical and sparse approximation Bayesian Inversion Bayesian optimal experimental design

A course on modern numerical methods for random partial differential equations