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DTSTART;TZID=America/Los_Angeles:20221107T161500
DTEND;TZID=America/Los_Angeles:20221107T171500
DTSTAMP:20260515T044555
CREATED:20220913T161358Z
LAST-MODIFIED:20230816T041034Z
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SUMMARY:Applied Math Seminar: Angel Chavez (Pomona)
DESCRIPTION:Title: Randomized Sums of Graph Spectra \nAbstract:\nThe adjacency matrix of an Erdős-Rényi-Gilbert graph is a random symmetric matrix whose entries are Bernoulli random variables. These entries\, modulo the constraints imposed by symmetry\, are independent. We aim to understand the asymptotic behavior of randomized sums of the spectra and singular spectra of these matrices. In particular\, we establish several central-limit type theorems for these randomized sums of eigenvalues and singular values.
URL:https://colleges.claremont.edu/ccms/event/applied-math-seminar-angel-chavez-pomona/
LOCATION:Shanahan 2407 at Harvey Mudd College\, Claremont\, CA\, 91711\, United States
CATEGORIES:Applied Math Seminar
ORGANIZER;CN="Heather Zinn Brooks":MAILTO:hzinnbrooks@g.hmc.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20221114T161500
DTEND;TZID=America/Los_Angeles:20221114T171500
DTSTAMP:20260515T044555
CREATED:20220919T154642Z
LAST-MODIFIED:20221101T152658Z
UID:2932-1668442500-1668446100@colleges.claremont.edu
SUMMARY:Applied Math Seminar: Jahrul Alum (Memorial University of Newfoundland)
DESCRIPTION:Title: Data-driven large eddy simulation of atmospheric turbulence \nAbstract: Over the last few years\, machine learning has been critical in science and engineering and emerged as a data-driven turbulence model. However\, machine learning depends on training data from previous experiments on turbulent flows. Typically\, training data capture only a fraction of the active scales of turbulence. Despite decades of research\, the best turbulence theory has yet to emerge\, which limits the training of supervised machine learning models. Reinforcement learning is one way to alleviate these challenges. A reinforcement learning model interacts directly with the dynamical system itself. In this talk\, I will use the Burgers equation to illustrate data-driven learning of dynamical systems. Then\, I use simulations of a NACA airfoil and a wind farm to outline the reinforcement learning framework. Finally\, the talk presents a proof of concept for optimizing large eddy simulation through reinforcement learning.
URL:https://colleges.claremont.edu/ccms/event/applied-math-seminar-jahrul-alum-memorial-university-of-newfoundland/
LOCATION:Shanahan 2407 at Harvey Mudd College\, Claremont\, CA\, 91711\, United States
CATEGORIES:Applied Math Seminar
ORGANIZER;CN="Heather Zinn Brooks":MAILTO:hzinnbrooks@g.hmc.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20221121T161500
DTEND;TZID=America/Los_Angeles:20221121T171500
DTSTAMP:20260515T044555
CREATED:20220905T171325Z
LAST-MODIFIED:20230816T040911Z
UID:2830-1669047300-1669050900@colleges.claremont.edu
SUMMARY:Applied Math Seminar: Junshan Lin (Auburn)
DESCRIPTION:Title: Scattering Resonances Through Subwavelength Holes and Their Applications in Imaging and Sensing \nAbstract:\nThe so-called extraordinary optical transmission (EOT) through metallic nanoholes has triggered extensive research in modern plasmonics and their applications in bio-sensing\, imaging\, etc. This talk aims to provide quantitative mathematical  theories to understand a variety of resonances that induce the EOT phenomenon and present mathematical studies for their applications in imaging and sensing. \nIn the first part of the talk\, based upon the layer potential technique\, asymptotic analysis and the homogenization theory\, I will present rigorous mathematical analysis to investigate the scattering resonances for several typical two-dimensional structures\, including Fabry-Perot resonance\, Fano resonance\, etc. In the second part of the talk\, mathematical studies for their applications in sensing and super-resolution imaging will be discussed. I will focus on the resonance frequency sensitivity analysis and how one can achieve super-resolution by using plasmonic nanohole structures.
URL:https://colleges.claremont.edu/ccms/event/applied-math-seminar-junshan-lin-auburn/
LOCATION:Shanahan 2407 at Harvey Mudd College\, Claremont\, CA\, 91711\, United States
CATEGORIES:Applied Math Seminar
ORGANIZER;CN="Heather Zinn Brooks":MAILTO:hzinnbrooks@g.hmc.edu
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=America/Los_Angeles:20221128T161500
DTEND;TZID=America/Los_Angeles:20221128T171500
DTSTAMP:20260515T044555
CREATED:20220920T153253Z
LAST-MODIFIED:20221122T174526Z
UID:2939-1669652100-1669655700@colleges.claremont.edu
SUMMARY:Applied Math Seminar: Juergen Kritschgau (Carnegie Mellon)
DESCRIPTION:Title: Using Mutual Information of Hypergraph Compressions for Clustering\n\nAbstract: Hypergraphs are often used to represent higher order observed relationships between subjects of study. In particular\, the vertices of a hypergraph could represent the basic elements of study\, and edges represent observed relationships between the vertices. Implicitly\, the assumption is that observed edges are more (or less) likely to appear between vertices that are “similar”. Therefore\, an important question in data science is whether the edges of a hypergraph can be used to recover ground truth vertex labels where two vertices receive the same label if they are similar. This is known as the clustering problem. In this talk\, we will discuss how mutual information of hypergraph compressions can be used to cluster hypergraphs\, and apply this clustering strategy to synthetic and real world data sets.
URL:https://colleges.claremont.edu/ccms/event/applied-math-seminar-juergen-kritschgau-carnegie-mellon/
LOCATION:Shanahan 2407 at Harvey Mudd College\, Claremont\, CA\, 91711\, United States
CATEGORIES:Applied Math Seminar
ORGANIZER;CN="Heather Zinn Brooks":MAILTO:hzinnbrooks@g.hmc.edu
END:VEVENT
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