Applied/ACMS

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Applied and Computational Mathematics Seminar


Fall 2024

Date Speaker Title Host(s)
Sep 13 Nan Chen (UW) Intro. to Uncertainty Quantification (UQ) (tutorial) Spagnolie
Sep 20 Katie Newhall (UNC Chapel Hill) Rycroft
Sep 27 Indresan Govender (Mintek / Univ. of KwaZulu-Natal, South Africa) Rycroft
Oct 4* Hongfei Chen (Tulane) Jean-Luc
Oct 11 Colloquium in B239 at 4:00pm Mikaela Iacobelli (ETH/IAS) TBA Li
Oct 18 Colloquium in B239 at 4:00pm Guillaume Bal (U Chicago) TBA Li, Stechmann
Oct 25 Anil Damle (Cornell) Li
Nov 1 Michael Sprague (NREL) TBA Spagnolie
Nov 8 Miranda Holmes-Cerfon (UBC) Stechmann
Nov 15* Yue Sun (UW–Madison) Rycroft
Nov 22 Ondrej Maxian (U Chicago) TBA Ohm & Spagnolie
Nov 29* Thanksgiving
Dec 6 Ido Lavi (Flatiron) TBA Spagnolie

Dates marked with an asterisk correspond to home football games of the UW–Madison Badgers. On these dates it can be difficult to get a hotel room close to campus at short notice.

Abstracts

Nan Chen (UW–Madison)

Title: Taming Uncertainty in a Complex World: The Rise of Uncertainty Quantification -- A Tutorial for Beginners

I will provide a tutorial about uncertainty quantification (UQ) for those who have no background but are interested in learning more about this area. The talk will exploit many elementary examples, which are understandable to graduate students and senior undergraduates, to present the ideas of UQ. Topics include characterizing uncertainties using information theory, UQ in linear and nonlinear dynamical systems, UQ via data assimilation, the role of uncertainty in diagnostics, and UQ in advancing efficient modeling. The surprisingly simple examples in each topic explain why and how UQ is essential. Both Matlab and Python codes have been made available for these simple examples.

Anil Damle (Cornell)

Title: Fine-grained Theory and Hybrid Algorithms for Randomized Numerical Linear Algebra

Randomized algorithms have gained increased prominence within numerical linear algebra and they play a key role in an ever-expanding range of problems driven by a breadth of scientific applications. In this talk we will explore two aspects of randomized algorithms by (1) providing experiments and accompanying theoretical analysis that demonstrate how their performance depends on matrix structures beyond singular values (such as coherence of singular subspaces), and (2) showing how to leverage those insights to build hybrid algorithms that blend favorable aspects of deterministic and randomized methods. A focus of this talk will be on methods that approximate matrices using subsets of columns. Relevant motivating applications will be discussed and numerical experiments will illuminate directions for further research.

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