# APDE: Seminars

## Seminars in Applied and Computational Analysis

## Uncertainty quantification in kinetic equations

Kinetic equations describe dynamics of probability density distributions of large number of particles, and have classical applications in rarefied gas, plasma, nuclear engineering and emerging ones in biological and social sciences. Since they are not first principle equations, rather are usually derived from mean field approximations of Newton’s second law, thus contain inevitably uncertainties in collision kernel, scattering coefficients, initial and boundary data, forcing and source terms. In this talk we will review a few recent results for kinetic equations with random uncertainties. We will extend hypocoercivity theory, developed for deterministic kinetic equations, to study local sensitivity, regularity, local time behavior of the solutions in the random space, and also establish corresponding theory for their numerical approximations.

- Part of Applied and Computational Analysis
- Speaker: Shi Jin (University of Wisconsin-Madison)
- Monday 21 May 2018, 14:00-15:00
- MR5, Centre for Mathematical Sciences.

## Geometric Structure of graph Laplacian embeddings

In this talk I will address theoretical questions related to the task of data clustering (unsupervised learning) and in particular about a concrete and popular methodology known as spectral clustering. In spectral clustering the idea is to first use the spectrum of a graph Laplacian associated to a point cloud to construct an embedding of the cloud into some Euclidean space; after the embedding step, an algorithm like k-means is used to obtain the desired clusters. Despite the popularity of the method and its intuitive understanding by practitioners, only very few rigorous mathematical results aiming to justify its use are available. During my talk I intend to give answers to the following theoretical questions: What is the geometry of these graph Laplacian embeddings as the number of data points goes to infinity, and what is special about them that makes spectral clustering a successful methodology? I will also discuss some of the computational consequences of the theoretical results that I will present.

A variety of mathematical tools from optimal transport, spectral geometry, meta stability, and probability makes the analysis possible. The talk is based on joint work with Bamdad Hosseini (Caltech) and Franca Hoffman (Caltech).

- Part of Applied and Computational Analysis
- Speaker: Nicolas Garcia Trillos, Brown University
- Thursday 14 June 2018, 15:00-16:00
- MR 5.

## TBC

TBC

- Part of Applied and Computational Analysis
- Speaker: Bernhard Schmitzer, University of Münster
- Thursday 12 July 2018, 14:00-15:00
- MR 5.

## TBC

TBC

- Part of Applied and Computational Analysis
- Speaker: Andrew Lam (Hong Kong)
- Thursday 16 August 2018, 15:00-16:00
- MR 5.