All Colloquia

Please join us for the Kick-Off Workshop of SFB 1294, where members of our collaborative research center and invited guests will discuss the newest…

Protein production is a key process of prokaryotic and eukaryotic cells consuming more that 80% of their resources. The cytoplasm of the cell being a…

The visual recognition of objects by humans in everyday life is typically rapid and effortless. Until very recently, animate visual systems were the…

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We introduce basic ideas and methods of data assimilation in meteorology and oceanography, and illustrate their progress from numerical weather…

We consider a stochastic Hodgkin-Huxley model where dendritic input -modelled as an autonomous SDE which depends on a deterministic T-periodic signal…

Kernel embeddings of distributions and the Maximum Mean Discrepancy (MMD), the resulting probability metric, are useful tools for fully nonparametric…

Earthquakes seldom come alone. They occur in temporal and spatial clusters, occasionally preceded by foreshocks, always followed by aftershocks, and…

The Legacy of Rudolph Kalman

In 1960 Rudolph Kalman published what is arguably the first paper to develop a systematic, principled approach to the…

Mathematical modelling has been key to understanding the mechanics of cell shapes and the theoretical principles behind the complex spatio-temporal…

This talk has been postponed- we will inform you about the new date in the Events section.

Particle filters hold the promise of fully nonlinear data…

A novel class of continuous-time non-reversible Markov chain Monte Carlo (MCMC) based on piecewise-deterministic processes has recently emerged. In…

Over the past decades physics-based data assimilation models have been used in many areas of science and engineering and have found extensive use in…

For new medicines to be approved for therapeutic use, three internationally accepted criteria have to be met: efficacy, safety, quality. Yet, in drug…

Felix Otto will speak as invited guest during this joint colloquium of SFB 1114 and SFB 1294 on the

Effective Behavior of random media

Abstract: In…

Parameter identification problems typically consist of a model equation, e.g. (systems of) ordinary or partial differential equations, and the…

Are event related potentials, well investigated under laboratory
conditions, a signature of cortical processing during natural behavior?
       We…

After giving a short introduction to biological neuronal networks and their
main properties, I will explain why neurobiologists are interested by
…

Raanes et al. (2019) revised the iterative ensemble smoother of Chen and Oliver (2013), denoted Ensemble Randomized Maximum Likelihood (EnRML), using…

Predictive data science for physical systems: From model reduction to scientific machine learning

Achieving predictive data science for physical…

The Hamiltonian Monte Carlo  (HMC) method is a widely used Markov Chain Monte Carlo algorithm that offers the possibility of combining high acceptance…

The concept of distance is ancient and ubiquitous. Being able to quantify the distance between objects has important applications everywhere in…

Weak constraint four-dimensional variational data assimilation is an important method for incorporating data (typically observations) into a model.…

Mobility is an important topic, because on the one hand, the global world requires mobility of people and goods, but on the other hand, we suffer from…

- Unfortunatly this colloquium will be cancelled due to the current situation concering the Corona Virus -

Suppose the PDE is cast in a variational…

We consider optimal sensor placement for inverse problems constrained by partial differential equations in which the model contains uncertainties,…

The talk is concerned with recent results on statistical properties of deterministic dynamical systems. We will discuss the problem of finding…

Developers of IT systems have enormous power. - And therefore also a great responsibility. This applies especially to the ideas and concepts about the…

The Lasso revisited: entropy bounds and dual certificates

The Lasso is least squares estimation with an ℓ₁-penalty on the coeffcients. In this talk…

We consider the problem of recovering the underlying truth in a nonparametric Bayesian inference setting with p-exponential priors. These priors are a…

Cognitive computational neuroscience of vision

To learn how cognition is implemented in the brain, we must build computational models that can…

Please find the abstract to the talk here.

Invited by Markus Reiß

***Due to the current pandemic this colloquium will be conducted online. We…

Unfortunately we had to cancel this Colloquium.

invited by Yuri Shprits

***Due to the current pandemic this colloquium will be conducted online.…

Like in all scientific disciplines research data in mathematics has become vast, it is complex and multifaceted, and, through the successful…

The class of integer-valued trawl processes has recently been introduced for modelling univariate and multivariate integer-valued time series with…

Almost 200 years ago, Alexander von Humboldt said in his famous Cosmos series that „all natural forces are linked together, and made mutually…

Cellular signal transduction system is a complex reaction network consisting of many elements, which  allows cells to respond appropriately to…

Quantitative epistemology: conceiving a new human-machine partnership

Quantitative epistemology is a new and transformational area of research…

Complex dynamical systems like molecular systems or stochastic climate systems often involve a variety of different time and length scales. We propose…

The use of AI has improved computer-aided diagnosis systems in many areas. However, the underlying models are often black boxes that allow the user…

Neutron stars are among the most compact objects in the Universe and the detection of gravitational waves and electromagnetic signals from the merger…

One of the main assumptions of classical machine learning models is that data are generated by a stationary concept. This is often violated due to…

In recent years there has been an increasing awareness of the risks of collapse or tipping points in a wide variety of complex systems, ranging from…

Collective dynamics can be observed in many situations in our daily lives, for example the motion of bird flocks, the formation of directional lanes…

Our Mercator Fellows Sean Meyn and Youssef M. Marzouk will visit us in the week from June 27th to July 1st. We will use this opportunity to have an…

Recent experimental advances enable high-resolution observations of biological and synthetic active matter across a wide range of length and time…

Nested cubing integration: how to get a O(N^{-10}) error when you compute your favourite integral

This talk will explain why computing integrals…

The Earth’s magnetosphere is a complex system composed of about a dozen major subsystems that are not only complex in-and-of themselves but…

The myocardium is an electrically excitable medium that supports various types of excitation waves, including stable or chaotic spiral waves that…

Approaches to decision making and learning mainly rely on optimization techniques to achieve “best” values for parameters and decision variables. In…

In my talk I will give an overview on our ongoing work on the development of methods for finding associations between variation that is encoded in…

Reinforcement Learning (RL) builds learning systems from the ground up by trial and error: as the agent takes actions, they receive rewards and…

Seasonal forecasts aim to provide physically-based probabilistic outlooks of the climate conditions for the coming seasons. We create them by…

My talk will be devoted to the convergence of stochastic interacting particle systems in the mean-field limit to solutions of conservative SPDEs. We…

This is a co-organized colloquium with the Institute of Mathematics of the UP

Scientific visualization is classically defined as the process of…

In this talk we will introduce a definition of 'research data' in mathematics which encompasses all digital objects you handle in the process of doing…

The use of model order reduction techniques in combination with data assimilation methods for estimating the state of systems has been of great…

This year, Kalman Lecture will be held by Richard Nickl from University of Cambridge. His talk on 'On posterior consistency in non-linear Bayesian…

We consider the problem of generating random samples from high-dimensional posterior distributions. We will discuss both (i) conditions under which…

We consider linear inverse problems under white (non-Gaussian) noise. For the solution we have to discretize the problem, and we consider a sequence…

The recent State of Global Water Resources report by the World Meteorological Organization concluded that the hydrological cycle is spinning out of…

Unlike traditional finite-dimensional stochastic differential equations, statistical models driven by SPDEs are predominantly singular, when the…

To grasp the meaning of words and their relationship to the outside world, higher cognitive processes unique to the human brain are at work. However,…

Our planet is undergoing an unprecedentedly rapid transformation. To predict future climate and environment dynamics, we have to learn from past…

We consider gradient flows with respect to the maximum mean discrepancy (MMD) of certain kernels. For the efficient computation, we propose slicing…

The talk will be concerned with time-fractional master equations with random transition probabilities describing a heterogeneous population of random…

In this talk, I will present a unifed approach for the effect of fast rotation and dispersion as an averaging mechanism for regularizing and…

7th Kálmán Lecture with Martin Burger

The use of methods resembling (interacting) particle systems has gained a lot of interest for different tasks…

Gaussian processes (GP) are frequently used in Bayesian nonparametrics as a prior distribution on infinite dimensional functional parameters. However,…

The need to know a few singular triplets associated with the largest singular values of a third-order tensor arises in data compression and extraction. This paper describes a new method for their computation using the t-product. Methods for deter mining a couple of singular triplets associated with the smallest singular values also are presented. The proposed methods generalize available restarted Lanczos bidiagonalization methods for computing a few of the largest or smallest singular triplets of a matrix. The methods of this paper use Ritz and harmonic Ritz lateral slices to determine accurate approximations of the largest and smallest singular triplets, respectively. Computed examples show applications to data compression and face recognition. more ›

Modeling and inference in multivariate time series is central in statistics, signal processing, and machine learning. A fundamental question when…

Biological systems process information despite noise-corrupted input, often operating at physical limits. A prime example is chemotaxis, i.e., active…