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Until today, the transition from laminar ows to fully turbulent ones remains a process which cannot be fully explained in fluid dynamics. In an extensive physical experiment, Lemoult et al. (2016) collect a large quantity of data on the development of turbulence in Couette ows. They then provide some evidence that it falls into the directed percolation universality class, one of the most popular models for non-equilibrium phase transitions in natural sciences. Using geostatistical tools, we analyse their data to nd further structural behaviour, showing similarities and differences between the transition to turbulence in incompressible uids and directed percolation.
Existing accounts of pejoratives converge on the idea that a use of a pejorative such as 'jerk' or 'asshole' is felicitous as long as the speaker has a negative attitude toward the person for whom they are using the pejorative, and does not place any special constraints on the conversational context. This talk presents experimental data that challenge this idea, based on joint work with Bianca Cepollaro and Filippo Domaneschi, "When is it OK to call someone a jerk? An experimental investigation of expressives", Synthese 2020, doi 10.1007/s11229-020-02633-z). Our main study shows that pejoratives are sensitive to contextual information to a much higher degree than the non-pejorative control items, both in their referential and (albeit less) their predicative use. I shall discuss the broader implications of these results, as well as several follow-up studies.
Meeting ID: 925-6562-2309, Password on demand: Office.Leitgeb@lrz.uni-muenchen.de
In social-ecological systems (SESs), social and biophysical dynamics interact within and between the levels of organization at multiple spatial and temporal scales. Cross-scale interactions (CSIs) are interdependences between processes at different scales, generating behaviour unpredictable at single scales. Understanding CSIs is important for improving SES sustainability, but they remain understudied. Theoretical models are needed that capture essential features while being simple enough to yield insights into mechanisms. In this talk, I will present a stylized model of CSIs in a two-level system of weakly interacting communities harvesting a common-pool resource. Community members adaptively conform to, or defect from, a norm of socially optimal harvesting, enforced through social sanctioning both within and between communities. We find that each subsystem’s dynamics depend sensitively on the other's despite interactions being much weaker between subsystems than within them. We identify conditions under which subsystem-level cooperation produces desirable system-level outcomes. Our findings expand evidence that social cooperation is important for sustainably managing shared resources, showing its importance even when resource sharing and social relationships are weak. Finally, I will briefly discuss philosophical and methodological challenges related to the concept of scale in social-ecological system modelling.
We study the totally asymmetric simple exclusion process (TASEP) on rooted trees. This means that particles are generated at the root and can only jump in the direction away from the root under the exclusion constraint. Our interests are two-fold. On the one hand, we study invariant measures for the TASEP on trees and provide sufficient conditions for the existence of non-trivial equilibrium distributions. On the other hand, we consider the evolution of the TASEP on trees when all sites are initially empty and study currents.This talk is based on joint work with Nina Gantert and Nicos Georgiou.
Variational integrators are derived for structure-preserving simulation of stochastic forced Hamiltonian systems. The derivation is based on a stochastic discrete Hamiltonian which approximates a type-II stochastic generating function for the stochastic flow of the Hamiltonian system. The generating function is obtained by introducing an appropriate stochastic action functional and considering a stochastic generalization of the deterministic Lagrange-d'Alembert principle. Our approach presents a general methodology to derive new structure-preserving numerical schemes. The resulting integrators satisfy a discrete version of the stochastic Lagrange-d'Alembert principle, and in the presence of symmetries, they also satisfy a discrete counterpart of Noether's theorem. Furthermore, mean-square and weak Lagrange-d'Alembert Runge-Kutta methods are proposed and tested numerically to demonstrate their superior long-time numerical stability and energy behavior compared to non-geometric methods. The Vlasov-Fokker-Planck equation is considered as one of the numerical test cases, and a new geometric approach to collisional kinetic plasmas is presented.
The talk will take place via zoom: https://tum-conf.zoom.us/j/94339247280 Meeting ID: 943 3924 7280 Passcode: 402216
The creation of adequate mathematical models is an integral part of the study of complex natural phenomena in mechanics, physics, biology, and medicine. Correct mathematical models form the necessary basis for the development of numerical methods and algorithms. This allows for computer simulations to identify and explain important patterns that are not always amenable to study using physical experiments. The workshop topics will cover mathematical and computer modeling for a wide range of problems of optimization, control theory, thermodynamics, materials science, biology, medicine, and aerospace.
List of Topics * Mathematical modeling and computer simulation of mechanical, physical, and biological processes * Optimization, control, and numerical methods for ordinary and partial differential equations * Computer technologies and data analysis in engineering and bioinformatics
Program: http://go.tum.de/882610 To attend the BBB-sessions please contact: kuttler@ma.tum.de, kovtanyu@ma.tum.de or turova@ma.tum.de Conference Webpage: https://easychair.org/cfp/MMSC-2020
In its historical forms, optimal transport looked like a very specialized topic, concerned with moving a pile of mortar efficiently to a range of target location on a construction site (Monge, 1781), or transferring the output of an array of steel mines optimally to a network of factories (Kantorovich, 1942). In the past three decades there has been an explosion of interest in the subject as this type of problem was found to arise in amazingly many different fields of mathematics, science, and engineering: fluid dynamics, probability theory, statistics, dissipative PDEs (in the 1980s and 1990s); functional inequalities, curved geometry, traffic flow, urban planning (in the 2000s); many-electron physics, image processing, crowd motion, data analysis, machine learning (in the 2010s).
In this mini-course of six 90-minute lectures, I will explain cornerstone concepts, results and methods of optimal transport such as Wasserstein distances, Kantorovich duality, Brenier's theorem, or the Sinkhorn algorithm, at other than a superficial level. I will build up the mathematical theory rigorously and from scratch, aided by intuitive arguments, informal discussion, and carefully selected applications.
Those interested, please email to clayton@ma.tum.de for full details on course times, zoom link etc
Two fundamental algorithmic tasks associated to discrete statistical mechanics models are approximate counting and approximate sampling. When correlations are weak (i.e., at sufficiently high temperatures) efficient algorithms for these tasks can be obtained by giving rigorous mixing time bounds for Markov chains. However, at low temperatures, when correlations are strong, mixing times can become impractically large, and Markov chain methods may fail to be efficient. Recently, the cluster expansion has been used to develop provably efficient low-temperature algorithms for some discrete statistical mechanics models. I’ll explain these algorithmic tasks, and how cluster expansion algorithms work, focusing on the particular case of the q-state Potts model on random regular graphs when q is large. If time permits, I’ll also discuss some new probabilistic results that were obtained as consequences of the development of these algorithms.Based on joint work with Matthew Jenssen and Will Perkins.
There’s an important connection between facts of the form making A true is better for me than making B true and facts about how good things would be for me had A been true and how good things would have been had B been true. However, it’s commonly assumed that the direction of analysis and explanation goes from the latter sorts of facts to the former. I will deny this, and develop a view according to which the counterfactual is defined by its role in thought.
Meeting ID: 925-6562-2309, Password on demand: office.leitgeb@lrz.uni-muenchen.de
We analyse the behaviour of a large system of weakly interacting diffusion processes evolving under the influence of a periodic interaction potential. We focus our attention on the combined mean field and diffusive (periodic homogenisation) limits. In particular, we show that these two limits do not commute if the mean field system quotiented to the torus undergoes a phase transition, that is to say if it admits more than one steady state. A typical example of such a system on the torus is given by the noisy Kuramoto model of mean field plane rotators.
In this talk, we study a family of self-similar interval-partition-valued diffusions with Poisson--Dirichlet pseudo-stationary distribution. Such diffusions arise as limits of certain up-down ordered Chinese restaurant processes and have applications to continuum random tree models. This talk is based on joint works (in progress) with Noah Forman, Douglas Rizzolo, and Matthias Winkel.