All Seminars

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Inverse problems for subsurface ow are typically characterized by large numbers of para- meters (e.g. coef cients of PDEs describing ow and transport)…

This talk is concerned with phase transition and self-organization in mean-field games.

The motivation comes from the following sequence of events…

The use of models and data, via data assimilation, is one ofthe strategies pursued to improve climate and weather predictions and retrodictions. In…

Fluid extraction from a subsurface hydrocarbon reservoir results in compaction of the reservoir, in particular cases leading to subsidence and induced…

We introduce a class of structure-exploiting nonlinear filters for high-dimensional state-space models with intractable transition kernels. The idea…

A short introduction into Deep Learning will be followed by an overview on Deep Generative Models. This will include energy based models (Boltzmann…

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After World War II, the Bretton Woods Conference designed a new international
monetary order, establishing the supremacy of the dollar, while…

We consider the minimax quickest detection problem of an unobservable time of proportional
change in the intensity of a doubly-stochastic Poisson…

Low-cost and noisy sensor sources in modern smartphones introduce both interesting possibilities for new applications, and challenges for inference…

Guidance of autonomous vehicles (AVs) poses different challenges for data assimilation methods than geophysical applications, requiring novel…

Markov Chain Monte Carlo methods are popular tools in Bayesian statistics and molecular dynamics to draw samples from a given probability…

This talk discusses stability of a class of Kalman-Bucy filters (including the classical extended Kalman-Bucy filter) for continuous-time systems with…

In this talk I will focus on one of our recent developments where we show how the Gaussian process (GP) can be used to solve stochastic optimization…

The node degrees of large real-world networks often follow a power-law distribution. Such scale-free networks can be social networks, internet…

After a short introduction into the history of Brownian motion I will present
the stochastic motion in several physical systems, in particular with…

In the first part of the talk we will discuss the parameter estimation problem using Bayesian approach for the drift coefficient of some linear …

This seminar aims to give a broad and straightforward presentation on fundamental aspects related to the theory and application of continuous-time…

It is well-accepted that the chaotic nature of atmospheric dynamics imposes an inherent finite limit of predictabil- ity. The idea originated from a…

Anthropogenic warming is anticipated to impact the hydrological cycle tremendously in the future. However, projections are accompanied by large…

The Schrödinger problem is an entropy minimization problem on a set of path measures with prescribed initial and final marginals. It arises from a…

High dimensionality often appears in data assimilation and Bayesian sampling problems.  It is prohibitive for most classical computational…

The presentation gives an overview about experimental and numerical approaches to analyze reactive transport processes in soils and groundwater.…

This talk is postponed due to illness. The new date will be announced under events.

Dieser Vortrag wird krankheitsbedingt leider nicht statt finden.…

This talk has been moved to Friday, the 09.11. at 10:15 in lecture hall 0.108, building 28, Campus Golm.

Mathematical modelling has been key to…

When sampling measures on Hilbert spaces one option is to first discretize the space and then apply standard Markov Chain Monte Carlo methods. This…

The coupling between stratospheric and tropospheric dynamics is currently a topic of major interest [1,2].  In the context of the Southern…

Scientific app development for implementing robotics controllers and machine learning algorithms has become much easier with the introduction of tools…

We propose a Hawkes point process model for point processes where the excitations are modulated via a Gaussian process prior with a sigmoid link…

Perception can be seen as unconscious inference in a dynamically changing environment and formalized as nonlinear Bayesian filtering. A heuristic…

Models of eye movement control differ in the extent to which fixation duration is controlled directly by…

Many physical problems, most importantly the quantification of climate change, involve estimating the response of a deterministic chaotic dynamical…

Sequential Monte Carlo methods (SMC) are typically stochastic. Ensemble Transform Particle filter (ETPF) is a deterministic SMC method. It, however,…

This talk is concerned with the long time behavior of particle filters and Ensemble Kalman filters. These filters can be interpreted as mean field…

A singular stochastic control problem typically describes the situation in which
an agent has to choose optimally an irreversible strategy in order to…

When looking for asymptotic results for some statistical model, global asymptotic equivalence, in the Le Cam sense, often proves to be a useful…

Although using probability distributions to model uncertainty is by far the most widely accepted approach, it does not come without inconveniences. A…

Talk by Julia Fleischer and Markus Seyfried

Das Projekt Kabinettwatch beschäftigt sich mit der Vorhersage der Zusammensetzung des Bundeskabinetts…

We extend the classical Dobrushin's uniqueness criterion to Markov random fields on general graphs and with single-spin spaces that need not be…

One of the main characteristics of infinite-dimensional dissipative evolution equations, such as the Navier-Stokes equations and reaction-diffusion…

We introduce sufficiently wide classes of non-linear functionals preserving normal (Gaussian) distribution and establish various conditions under…

A fundamental task in numerical computation is the solution of large linear systems, and iterative methods are among the most widely used solvers for…

We study a class of interacting particle systems that may be used for optimization. By considering the mean-field limit one obtains a nonlinear…

Convolutional Neural Networks (CNN) have recently seen tremendous success in various computer vision tasks. However, their application to problems…

Methods from data assimilation, inverse problems, and machine learning have shown exciting potential for transforming biomedicine.

First, I will show…

There is a certain magic involved in recasting the equations in Physics, and the algorithms in Engineering, in variational terms.  The most classical…

Neo-Narval at TBL/Pic du Midi (France) will be the first instrument working simultaneously in high resolution spectroscopy, polarimetry and…

Coupled models simulate different compartments of the Earth system as well as their interactions. For example coupled ocean-biogoechemical models…

In this talk we will present a data assimilation problem based on a new stochastic rotating shallow
water (SRSW) signal and an adaptive tempering…

We consider the problem of maximizing a non-concave Lipschitz function f over a bounded domain in dimension d. In this talk we provide regret…

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Bayesian Inference is almost always a very challenging mathematical and computational problem. In the context of Gaussian Process, only a Gaussian…

n many physical systems, measurements can only be obtained on the exterior of an object (e.g., the human body or the earth's crust), and the goal is…

Mathematical modeling has been a key tool in various scientific fields (such as biology, medicine, and engineering) in understanding systems dynamics.…

Recent progress in machine learning has shown how to forecast and, to some extent, learn the dynamics of a model from observations, resorting in…

Slow-fast systems consist of slow macroscopic and fast microscopic dynamics. By using equation-free methods, one can do a complete bifurcation…

Gaussian processes are valuable tools for non-parametric modelling, where typically an assumption of stationarity is employed. While removing this…

We discuss methods for creating Gaussian likelihoods for data that does not directly follow any known statistics. Obvious summary statistics are…

 Atmospheric dispersal of contaminants such as ash can be modelled by stochastic differential equations coupled to a large-scale weather model. We…

The ensemble Kalman filter (EnKF) is a Monte-Carlo-based sequential filtering
method that is often both robust and efficient, but its performance may…

PDE-constrained optimization problems arise in many applications, including inverse problems and optimal control. As optimization over both the…

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Adaptive importance samplers are adaptive Monte Carlo algorithms to estimate expectations with respect to some target distribution which…

So far, our statistical understanding of the learning mechanisms of deep neural networks. (DNNs) is rather limited. Part of the reasons for this lack…

Signaling is governed not only by the expression levels of molecules, but by their localization via mechanisms as diverse as compartmentalization in…

jointly organised by Jana de Wiljes and the Lappeenranta-Lahti University of Technology (LUT, Finland) more ›

Statistical inference for binary data is one of the central problem in machine learning which can be treated within a nonparametric Bernoulli model.…

In today’s complex world and economy, international supply chains of goods cause global environmental impacts. Life Cycle Assessment (LCA) is a well…

Stochastic gradient descent is an optimisation method that combines classical gradient descent with random subsampling within the target functional.…

For partial differential equations with random coefficients we investigate the sensitivity of the distribution of the random solution with respect to…

tba

invited by Carsten Beta

We consider two sets of new priors for Bayesian inversion and machine learning: The first one is based on mixture of experts models with Gaussian…

Various modelling situations – including chaotic dynamics, stochastic differential equations, random patterns such as produced by the Turing…

We propose a systematic | theoretical | statistical analysis for systems of interacting
diffusions, possibly with common noise and/or degenerate…

We consider a linear ill-posed equation in the Hilbert space setting under white noise. Known convergence results for the discrepancy principle are…

A theory of parametric inference for semilinear stochastic partial differential equations is developed, with special emphasis put on diffusivity…

In this talk, I revisit the problem of filter stability based on certain dual optimal control-type reformulations of the nonlinear filtering problem.
…

Hypothesis testing in high dimensions has been a most active research topics in the last
decade. Both theoretical and practical considerations make it…

I will discuss a new neural network based prior for real valued functions on ℝ^d. The new prior is a Gaussian neural network prior, where each weight…

In this talk, I will present the dual control system for the hidden Markov model (HMM). This is a direct extension of classical duality between…

Jonas Latz:

Optimisation problems with discrete and continuous data appear in statistical estimation, machine learning, functional data science,…

Simple stochastic models can describe the fluctuations of a time series. A good way to get a first impression of the data is to look at the mean…

Visualizing and quantifying the properties of a medium using sparse indirect measurements is the final goal of all the imaging methods in different…

Kernel matrices are crucial in many learning tasks and typically dense and large-scale. Depending on the dimension of the feature space even the…

This workshop is part of the SFB IRTG Certificate Program and will be held by Dr. Peter Schröder. The onsite workshop wil deal with Good Scientif…

Abstract by Felipe Uribe:

In inverse problems arising in imaging science characterization of sharp edges in the solution is desired. Within the…

Abstract by Svetlana Dubinkina:

Data assimilation of high-dimensional nonlinear models is subject to curse of dimensionality. It is when an ensemble…

Inverse problems are ubiquitous in many fields of science such as engineering, biology, medical imaging, atmospheric science, and geophysics. Three…

Hurst index determines regularity, self-similarity and autocovariance structure of a fractional Brownian motion (fBm). Although there is a vast…

This workshop on Presentation Skills for Science and Research will be held by Dr. Peter Schröder and is mandatory for Doctoral researchers of our…

This is Part II of the workshop 'Presentation Skills for Science and Research' and held by Dr. Peter Schröder. This workshop day is splitted in 3…

Fractional Brownian motion (fBm) has been considered as a stochastic process generating autocorrelated (colored) Gaussian noise in various stochastic…

The reduced basis method is a certified model order reduction technique for the rapid and reliable solution of parametrized partial differential…

Our CRC Networking Meeting with Karen Veroy-GRepl and Martin Grepl provides a chance to talk about career, chances, and the challenges for couples in…

Dissipative nonlinear waves arise in many systems that comprise non-equilibrium properties, such as action potentials, calcium waves,…

Simon Barthelmé (CNRS, Gipsa-lab), joint work with K. Usevich, N. Tremblay, P.-O. Amblard

Kernel matrices are ubiquitous in statistics, numerical…

First session (07.12.; 09:00 - 12:30)

In the first session, this workshop focuses on what mental health is, what widespread mental health issues…

Second session (08.12.; 09:00 - 12:30)
The second session focuses on the concept of resilience: what exactly is it, how does it serve us and how can…

Third session (12.12.; 19:00 - 21:00)

In this meeting you reflect together about your progress/current situation in an informal setting.

In this talk, I look at two examples regarding the numerical computation of spectra for infinite-dimensional non-self-adjoint operators. Two…

Date: February 8th & 9th, 2024

The workshop with Anna Royon-Weigelt covers basic aspects of communication, especially in intercultural contexts and…

The workshop with Anna Royon-Weigelt covers basic aspects of communication, especially in intercultural contexts and presents tools & models to…

Importance sampling is a Monte Carlo technique that estimates posterior expectations in Bayesian computation by sampling from a tractable proposal…

Self-organization of various cellular structures is of fundamental importance in morphogenesis during development of biological organisms. Our…

We begin with a class of particle filters (PFs) that can automatically tune their performance by the online evaluation of certain predictive…

Our IRTG Networking Meeting with Joaquín Miguez and David Albers provides a chance to talk about career, chances, and the challenges in academia.

We would like to use data collected from humans to forward our understanding of physiological mechanics and to improve human health. However, because…

The Jamboree 2023 for all members of the IRTG as well as associated members will take place from march 13th to 15th at the Waldhotel Berghof. This…

Seminar talk followed by a young researcher network meeting.

Abstract:

The common solution techniques of the data assimilation problem typically…

Dr Martina Michalikova gives a two-day scientific writing workshop on the 6th and 7th of June 2024, from 9 a.m. - 5 p.m. The workshop focuses on…