January 2022

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TBA

### 10.01.2022 16:30 Sarah Penington (University of Bath):Branching random walk with non-local competitionOnline: attend

We study a particle system in which particles reproduce, move randomly in space, and compete with each other. We prove global survival as well as a shape theorem describing the asymptotic spread of the population, when the population density is sufficiently large. In contrast to most previous studies, we allow the competition kernel to have an arbitrary, or even infinite range, whence the term 'non-local competition'. This makes the particle system non-monotone and of infinite-range dependence, meaning that the usual comparison arguments break down and have to be replaced by a more hands-on approach. Based on joint work with Pascal Maillard.

### 19.01.2022 12:30 Tobias Windisch (Robert Bosch GmbH):Learning Bayesian networks on high-dimensional manufacturing dataOnline: attend

In our manufacturing plants, many tens of thousands of components for the automotive industry, like cameras or brake boosters, are produced each day. For many of our products, thousands of quality measurements are collected and checked during their assembly process individually. Understanding the relations and interconnections between those measurements is key to obtain a high production uptime and keep scrap at a minimum. Graphical models, like Bayesian networks, provide a rich statistical framework to investigate these relationships, not alone because they represent them as a graph. However, learning their graph structure is an NP-hard problem and most existing algorithms designed to either deal with a small number of variables or a small number of observations. On our datasets, with many thousands of variables and many hundreds of thousands of observations, classic learning algorithms don’t converge. In this talk, we show how we use an adapted version of the NOTEARs algorithm that uses mixture density neural networks to learn the structure of Bayesian networks even for very high-dimensional manufacturing data.

### 26.01.2022 13:00 Oliver Tse, Eindhoven University of Technology :Adjoint-based mean-field optimal controlOnline: attend (Code 101816)MI 02.08.011 (Boltzmannstr. 3, 85748 Garching)

In this talk we describe optimal control problems on two scales—micro- and mesoscopic scales—and discuss the appropriate adjoint-based calculus to use when one looks for quantitative estimates for the convergence of optimal controls in the mean-field limit. We further provide indications of when classical tools for PDE optimization is applicable. Several simulations are shown to underline the theory.

Link and Passcode: https://tum-conf.zoom.us/j/96536097137 Code 101816

### 31.01.2022 15:00 Dr. Konstantin Clauß:Local random wave model for semiclassical fractal structure of chaotic resonance states via ZOOM (Boltzmannstr. 3, 85748 Garching)

The semiclassical structure of resonance states of classically chaotic scattering systems with partial escape is investigated. We introduce a local randomization on phase space for the baker map with escape, which separates the smallest multifractal scale from the scale of the Planck cell [1]. This allows for deriving a semiclassical description of resonance states based on a local random wave model and conditional invariance. We numerically demonstrate that the resulting classical measures perfectly describe resonance states of all decay rates $\gamma$ for the randomized baker map. Comparison to the baker map without randomization shows very good agreement of the multifractal structures. Quantitative differences indicate that a semiclassical description for systems without randomization must take into account that the multifractal structures persist down to the Planck scale.

TBA