Dear friends of hikes and mathematics,

you are cordially invited to the extreme 46th mathematical hike planned on Saturday, July 13. Meet at 06:03 in Maloměřice, at the terminus ("Babická") of tram 4 (49.2284231N, 16.6532242E).

The goal is to walk 40 km to the train station, return to Brno by train. There will be plenty of possibilities to disconnect early.

Weigh your strength and join us, even if only for part of it.

Looking forward to see you,

Pavel Francírek and Jonatan Kolegar, organizers

If you have questions, send them to This e-mail address is being protected from spambots. You need JavaScript enabled to view it .

SCI MUNI, DMS, building 8, lecture room M1, June 6 – June 8, 2024

This mini-conference is organized in honour of Peter Michor's, who recently got his dr.h.c from our university. All the speakers are Peter's former students, they display an exciting variety of topics and they come from world top places (Kings College, Florida, ETH Zurich, etc).

All the talks will be in M1 and you are all cordially invited.

See the (preliminary) programme.

On 22.5.2024 in the meeting room in the DMS from 2pm will take place this lecture:

Dr. Sebastien Court (Institut für Mathematik, Universität Innsbruck)

Designing the monodomain model with artificial neural networks

Abstract:
We propose a data-driven method in order to identify the nonlinearity in the monodomain model. The monodomain model is a system coupling a semilinear parabolic PDE with an ODE, describing the time evolution of an electric potential. Our approach provides a general answer to the problem of selecting the model when studying phenomena related to cardiac electrophysiology: From measurements, instead of determining coefficients of a prescribed nonlinearity (like the FitzHugh-Nagumo model for instance), we design the nonlinearity itself, in the form of an artificial neural network (ANN), more specifically a feedforward residual network. The relevance of this approach relies on the approximation capacities of neural networks. Training the ANN corresponds to solving an identification problem constrained by the monodomain model so parameterized. We formulate this inverse problem as an optimal control problem, and provide mathematical analysis and derivation of optimality conditions for identifying the weights of the ANN. One of the difficulties comes from the lack of smoothness of activation functions which are classically used for training deep neural networks. We will also present numerical results that demonstrate the feasibility of the strategy proposed in this work. This is joint work with Prof. Karl Kunisch (RICAM & University of Graz).

Organizers:
doc. RNDr. Lenka Přibylová, Ph.D.
Department of Mathematics and Statistics, SCI MUNI
the Nonlinear Dynamics Team
https://science.math.muni.cz/ndteam/
Funding: European Horizon MSCA grant 101063853

Lectures between 13.5. - 15.5.2024, DMS building (MP2 and seminar room, 2nd floor )

Bayesian Data Analysis with R

Dr. Pablo Emilio Verde, Heinrich Heine University Düsseldorf

13.5.2024:

• Introduction to Bayesian inference
• Bayesian statistical of simple statistical models
• Using R, MultiBUGS and JAGS for simple Bayesian models

13:00 - 14:00, MP2
14:00 - 18:00, seminar room, 2nd floor

14.5.2024:

• The role of prior distributions in Bayesian inference
• Bayesian analysis of multiple parameters models
• Bayesian analysis of regression models
• Introduction to Bayesian computations

08:00 - 12:00, 13:00 - 14:00, 15:00 - 18:00, seminar room, 2nd floor

15.5.2024:

• Introduction to Hierarchical Modeling
• Longitudinal data analysis
• Bayesian analysis of special models: models for missing data, non-parametric models

10:00 - 12:00, 13:00 - 17:00, seminar room, 2nd floor