# All Seminars

### Adaptive convergence results for numerical efficient Bayesian methods

Jan van Waaj, University of Amsterdam, Netherlands 2.28.0.108

### Minimization-based sampling from the posterior distribution for inverse problems with Gaussian prior distributions

Dean S. Oliver, Uni Centre for Integrated Petroleum Research, Bergen, Norway 2.09.0.1410:15-11:45

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

more ›### Phase transition in mean-field games: An application to synchronization of coupled oscillators

Prashant Mehta, University of Illinois at Urbana-Champaign, USA 2.09.014Begin: 14:00

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

The motivation comes from the following sequence of events…

more ›### Improving Data Assimilation by Surfing the Waves

Juan Restrepo, Oregon State University, USA 2.28.2.12310:15-11:45

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

more ›### On the use of the particle filter for geomechanical parameter estimation when monitoring subsidence due to hydrocarbon extraction

Femke Vossepoel, Delft University of Technology, Netherlands 2.28.0.10410:15-11:15

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

more ›### A class of nonlinear filters induced by local couplings

Alessio Spantini, Massachusetts Institute of Technology (MIT), USA 2.28.0.10410:15-11:15

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

more ›### Generative Deep Learning

Sebastian Stober, University of Potsdam 2.28.0.104

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

more ›### Multi-stability & Fixational Eye Movements: an energy potential modeling approach

Kevin Parisot, GIPSA-Lab Grenoble 2.14.4.15/1610:15 - 11:45

### Financial Market History: Reflections on the Past for Applied Mathematician today

Nicole El Karoui, Ecole Polytechnique, France 2.9.0.129:15 - 10:15

After World War II, the Bretton Woods Conference designed a new international

monetary order, establishing the supremacy of the dollar, while…

### Minimax optimality in Robust Detection of disorder times in Doubly Stochastic Poisson Process

Nicole El Karoui, Ecole Polytechnique, Franc 2.9.0.1210:45 - 11:45

We consider the minimax quickest detection problem of an unobservable time of proportional

change in the intensity of a doubly-stochastic Poisson…

### Machine Learning for Sensor Fusion in Positioning and Navigation

Arno Solin, Aalto University, Finland 2.28.0.10410:00 - 11:00

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

more ›### Data Assimilation in Autonomous Vehicles

Erin Linebarger, University of Utah, USA 2.28.0.10411:00 - 12:00

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

more ›### Improving MCMC samplers with spectral theory and optimal transport

Nikolas Nüsken, Imerpial College London, UK 2.28.0.10410:15-11:45

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

more ›### On stability of a class of Kalman - Bucy filters for systems with non - linear dynamics

Toni Karvonen, Aalto University, Finland 2.28.0.10410:15-11:15

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

more ›### Composing stochastic quasi-Newton-Type algorithms

Thomas Schön, Uppsala University 2.28.0.10413:00 - 14:00

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…

more ›### The Intrinsic Geometry of Scale - Free Networks

Tobias Friedrich, Hasso Plattner Institute 2.09.0.1310:15-11:15

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

more ›### Thermodynamic limit and phase transitions in non-cooperative games: some mean-field examples

Paolo dai Pra, University of Padua 2.09.0.1316:15 - 17:45

In stochastic dynamics inspired by Statistical Mechanics the interaction between different particles, or agents, is usually …

### A nonparametric estimation problem for linear SPDEs

Randolf Altmeyer/ Markus Reiß, HU Berlin WIAS, Erhard-Schmidt Hörsaal, Mohrenstraße 39, 10117 Berlin10:00 - 12:30

It is well-known that parameters in the drift part of a stochastic ordinary differential equation, observed continuously on a time interval [0, T ],…

### Measures for diffusion, ergodicity & ageing

Ralf Metzler, Universität Potsdam 2.09.0.1310:15 - 11:45

After a short introduction into the history of Brownian motion I will present

the stochastic motion in several physical systems, in particular with…

### Parameter estimation problems for parabolic SPDEs

Igor Cialenco, Illinois Institute of Technology Weierstrass-Institute for Applied Analysis and Stochastics, Erhard-Schmidt-Hörsaal, Mohrenstraße 39, 10117 Berlin10:00 -12:30

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

more ›### Overview on stochastic models, McKean-Vlasov dynamics and their applications

Jean-Francois Jabir, National Research University Higher School of Economics, Moscow, Russia

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

more ›### Finite or infinite predictability horizon?

Tsz Yan Leung, University of Readings, UK 2.09.0.1310:15-11:00

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

more ›### Balancing robustness and accuracy in high resolution hydrological models

Sabine Attinger, Universität Potsdam 2.09.0.1310:15-11:45

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

more ›### Entropic and optimal transport

Christian Léonard, Université Paris Nanterre 2.09.2.2217:15

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

more ›### Localization for high dimensional data assimilation and MCMC

Xin Tong, National University of Singapore 2.09.0.1310:15 - 11:45

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

more ›### Integrated approaches to investigate reactive transport processes in soil and groundwater

Irina Engelhardt, Technische Universität Berlin 2.09.0.1210:15 - 11:45

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

more ›### Postponed due to illness- Kabinettwatch: Wer wird was im Bundeskabinett?

Markus Seyfried, Universität Potsdam, Germany 2.9.0.1410:15 - 11:45

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

Dieser Vortrag wird krankheitsbedingt leider nicht statt finden.…

more ›### Talk moved to Friday, 09.11. 10:15 am - Image-based modelling of problems in cell motility

Till Bretschneider, The University of Warwick, UK 2.28.2.12310:15 -11:45

*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…

more ›### Hamiltonian Monte Carlo methods on Hilbert spaces

Jakiw Pidstrigach, University of Bonn 2.9.0.1410:15 - 11:00

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

more ›### A Causal approach to spring-to-summer climate variability in the Southern Hemisphere

Elena Saggioro, University of Reading, UK 2.9.0.1411:00 - 11:45

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

more ›### Dances with Drones: Using Google’s TFLite for Autonomous Control of Aerial Drones by Gesture Recognition

Erin Linebarger, University of Utah (US) and SFB 1294, University of Potsdam 2.9.0.1410:15 - 11:45

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

more ›### Hawkes Processes with Sigmoid Gaussian Excitations

César Ojeda, Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS 2.9.0.1411:00 - 11:45

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

more ›### Neural Particle Filter and Beyond

Simone Surace, University of Zürich, ETH Zürich and University of Bern, Switzerland 2.9.0.1410:15 - 11:00

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

more ›### FEAT: Fixation control by Evidence Accumulation to Threshold

Casimir Ludwig, University of Bristol 2.14.4.06/0716:15

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

more ›### Validity of linear response theory in high-dimensional deterministic dynamical systems

Caroline Wormell, University of Sydney and SFB visiting PhD research fellow 2.09.0.1310:15-11:45

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

more ›### Deterministic Sequential Monte Carlo for non-Gaussian elliptic problems

Sangeetika Ruchi, Centrum Wiskunde & Informatica, Amsterdam, The Netherlands 2.09.0.1315:15 - 16:45

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

more ›### Uniform estimates for particle filters

Pierre del Moral, INRIA, Bordeaux Research Center, University of Bordeaux, France 2.09.0.1410:15-11:45

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

more ›### Nash Equilibria for Stochastic Games with Singular Control.

Jodi Dianetti, Universität Bielefeld 2.9.2.2210:15-11:45

A singular stochastic control problem typically describes the situation in which

an agent has to choose optimally an irreversible strategy in order to…

### Asymptotic equivalence for diffusion processes and the corresponding Euler scheme

Ester Mariucci, Universität Potsdam, SFB 1294, Germany 2.9.0.1410:15 - 11:15

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

more ›### Assimilating data with outer probability measures

Jeremie Houssineau, University of Warwick, UK 2.9.2.2213:00 - 14:00

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

more ›### Kabinettwatch: Wer wird was im Bundeskabinett?

Julia Fleischer, Universität Potsdam 2.09.0.1210:15 - 11:15

Talk by Julia Fleischer and Markus Seyfried

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

more ›### An extension of Dobrushin's uniqueness criterion and applications to Gibbs point processes

Tanja Pasurek, Universität Bielefeld, Germany 2.09.0.1312:00

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

more ›### Downscaling Data Assimilation Algorithm for Dissipative Evolution Models Employing Coarse Mesh Observables

Edriss Titi, Texas A&M University, USA 2.09.0.1210:15 - 11:15

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

more ›### Non–linear functionals preserving normal distribution and their asymptotic normality

Linda Khachatryan, Institute of Mathematics of the National Academy of Science of RA, Yerevan, Armenia 2.09.0.1312:00

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

more ›### Probabilistic Linear Solvers

Jon Cockayne, University of Warwick, UK 2.14.0.2110:15 - 11:15

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

more ›### Kalman-Wasserstein Gradient Flows

Franca Hoffmann, California Institute of Technology, USA 2.09.0.12/1310:15 - 11:15

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

more ›### Fully Hyperbolic Convolutional Neural Networks

Eldad Haber, The University of British Colombia, Canada 2.28.0.10810:15-11:15

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

more ›### Applied Data Assimilation: diabetes phenotyping/forecasting + hybrid machine learning approaches

Matthew Levine, California Institute of Technology, USA 2.09.0.1210:15 - 11:15

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

First, I will show…

more ›### What is the Lagrangian for Nonlinear Filtering?

Prashant Mehta, University of Illinois, USA 2.28.0.10210:15 - 11:15

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

more ›### Stellar Astrophysics: the power of simultaneous high resolution stellar spectroscopy, polarimetry and velocimetry. - Rotation, activity and stellar magnetic fields in the A0 standard star Vega

Torsten Böhm, Université de Toulouse 2.9.0.1211:00 - 12:00

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

more ›### Ensemble Data Assimilation for Coupled Models of the Earth System

Lars Nerger, Alfred-Wegener-Institute, Helmholtz Centre for Polar and Marine Science, Bremerhaven, Germany 2.9.0.1210:15- 11:15

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

more ›### Data assimilation for the stochastic one-layer rotating shallow water system driven by transport noise

Oana Lang, Imperial College London, UK 2.14.0.0910:15 -11:15

In this talk we will present a data assimilation problem based on a new stochastic rotating shallow

water (SRSW) signal and an adaptive tempering…

### Regret analysis of the Piyavskii-Shubert algorithm

Sébastien Gerchinovitz, IRT Saint-Exupéry, Toulouse, France 2.12.0.01 (large Lecture hall)10:15 - 11:15

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…

more ›### Using data assimilation, systems physiology, and healthcare data to forecast physiology in an intensive care unit: why it is important, what is possible, what is hard, and the state of the art

David Albers, University of Colorado, USA 2.9.2.2215:00 - 16:00

### Bayesian Inference Made Easy via Auxiliary Augmentations

Theo Galy-Fajou, Technische Universität Berlin 2.14.0.26/2712:30 - 14:00

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

more ›### Advancements in Hybrid Iterative Methods for Inverse Problems

Julianne Chung, Virginia Tech 2.29.0.25/0.2610:00 - 11:00

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…

more ›### Challenges in Dynamical Systems Inference: New Approaches for Parameter and Uncertainty Estimation

Matthias Chung, Virginia Tech 2.29.0.25/0.2611:00 - 12:00

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

more ›### Data-driven reconstruction of chaotic dynamics using data assimilation and machine learning

Marc Bocquet, École des Ponts ParisTech, France 2.26.0.7610:15 - 11:15

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

more ›### Implicit equation-free methods applied on noisy slow-fast systems

Anna Dittus, Universität Rostock TU Berlin Mathematikgebäude Raum MA74814:15 - 15:15

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

more ›### Posterior Inference for Sparse Hierarchical Non-stationary Models

Lassi Roininen, University of Oulu, Finland 2.9.0.1310:00 - 11:00

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

more ›### Statistics for chaotic dynamics and random patterns

Heikki Haario, LUT University (Technische Universität Lappeenranta), Finland 2.9.0.1311:00 - 12:00

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

more ›### Contaminant dispersal, numerical simulation, and stochastic PDEs

Tony Shardlow, University of Bath, UK 2.9.0.1213:00 - 14:00

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

more ›### Multilevel ensemble Kalman filtering algorithms

Hakon Hoel, RWTH Aachen 2.9.0.1310:15 - 11:15

The ensemble Kalman filter (EnKF) is a Monte-Carlo-based sequential filtering

method that is often both robust and efficient, but its performance may…

### Relaxation techniques for PDE-constrained optimization in inverse problems

Tristan van Leeuwen, Universiteit Utrecht, The Netherlands 2.9.0.1310:15 - 11:15

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

more ›### -Cancelled- Convergence rates for optimised adaptive importance samplers

Ömer Deniz Akyıldız, Universtiy of Warwick 2.09.0.1310:15 - 11:15

-Cancelled-

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

more ›### Some thoughts and questions towards a statistical understanding of DNNs

Ingo Steinwart, Universität Stuttgart online10:00 - 12:00

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

more ›### Regulation of Intracellular Signaling via Cellular Morphology

Meghan Driscoll , University of Texas Southwestern Medical Center, US online5:00 - 6:00 pm

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

more ›### Mini seminar series on „Non-Gaussian large scale Bayesian inversion“

Jarkko Suuronen, Sahani Pathiraja, Teemu Härkönen, LUT and UP online12:00 - 13:30

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

### Bayesian inference in machine learning

Vladimir Spokoiny, WIAS Berlin online10:00 - 12:00

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

more ›### Global sensitivity analysis in environmental modeling

Kim Aleksandra, ETH Zürich and Paul Scherrer Institute, Switzerland online10:15 -11:15

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

more ›### Analysis of stochastic gradient descent in continuous time

Jonas Latz, University of Cambridge, UK online10:15 - 11:15

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

more ›### Stability of UQ and Bayesian inverse problems

Bjoern Sprungk, TU Bergakademie Freiberg online10:15 - 11:15

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

more ›### Theoretical perspectives on actin waves

Arik Yochelis, Ben-Gurion University of the Negev, Israel 2.28.0.10810:15 - 11:45

tba

invited by Carsten Beta

more ›### On recently developed non-Gaussian priors and sampling methods with application to industrial tomography

Lassi Roininen, Technische Universität Lappeenranta, Finland 2.14.0.2110:15 - 11:15

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…

more ›### Gaussian likelihoods for ’intractable’ situations

Heikki Haario, Technische Universität Lappeenranta, Finland 2.28.0.108 (Campus Golm, building 28, room 0.108)10:00 - 11:00

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

more ›### Some statistical inference results for interacting particle models in a mean-field limit

Marc Hoffmann, Université Paris-Dauphine, France hybrid event, in person at WIAS (please register) and online; please contact Andrea Fiebig (fiebig[at]math.hu-berlin.de) for details10:00 - 12:00

We propose a systematic | theoretical | statistical analysis for systems of interacting

diffusions, possibly with common noise and/or degenerate…

### A modified discrepancy principle to attain optimal rates under white noise

Tim Jahn, Universität Bonn, Germany HU, Johann von Neumann-Haus (Rudower Chaussee 25), room: 3.00813:15 - 14:45

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

more ›### Parameter Estimation for Semilinear Stochastic Partial Differential Equations

Gregor Pasemann, Humboldt-Universität zu Berlin online via zoom, please contact Prof. S. Reich for the link10:15 - 11:15

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

more ›### A duality formulation for filter stability

Jin Won Kim, University of Illinois at Urbana-Champaign, USA online via Zoom, pls contact Wilhelm Stannat if you would like to join16:15

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 on high-dimensional spheres: the Le Cam approach

Davy Paindaveine, Univerité libres de Bruxelles, Belgium hybrid, online- please contact Andrea Fiebig (HU) for the zoom link10:00 - 12:00

Hypothesis testing in high dimensions has been a most active research topics in the last

decade. Both theoretical and practical considerations make it…

### Trace-class Gaussian priors for Bayesian learning of neural networks with MCMC

Torben Sell, University of Edinburgh, Scotland online, please contact us beforehand if you would like to join10:15 - 11:30

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…

more ›### On the duality for nonlinear filtering

Jin W. Kim, University of Illinois at Urbana-Champaign, USA Campus Golm, Building 9, Room 2.2210:00 - 11:30

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…

more ›### "Stochastic gradient descent in continuous time: discrete and continuous data" and "Augmenting Bayesian inference with possibility theory"

Jonas Latz (Heriot-Watt University, Edinburgh) & Jeremie Houssineau (University of Warwick, UK) Campus Golm, Building 5, Room 1.1010:00-12:00

**Jonas Latz:**

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

more ›### Stochastic Modelling with few Parameters

Philipp Meyer, UFS Data-centric Sciences, University of Potsdam Campus Golm, Building 9, Room 0.1210:15-11:15

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…

more ›### Toward a unified framework for large scale imaging problems: Theory, applications, and potential issues

Hossein S. Aghamiry, Université Côte d'Azur, Geoazur, Valbonne, France Campus Golm, Building 9, Room 1.1012:15-13:15

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

more ›### Learning in High-Dimensional Feature Spaces Using ANOVA-Based Fast Matrix-Vector Multiplication

Theresa Wagner, TU Chemnitz Campus Golm, building 9, room 0.1716:00-17:00

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

more ›### IRTG Workshop - Good Scientific Practice

Dr. Peter Schröder Campus Golm, building 29, room 0.25/0.2610:00-17:00

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…

more ›### "Two prior models for edge-preserving Bayesian inversion" & "Geometry Parameter Estimation for Sparse X-ray Log Imaging"

Felipe Uribe & Angelina Senchukova, LUT University, Finland Campus Golm, building 9, room 1.2210:15 - 11:15

**Abstract by Felipe Uribe:**

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

more ›### 'Projected Particle Filtering' and 'On ensemble size in a particle method for subsidence estimation'

Svetlana Dubinkina (VU Amsterdam) and Femke C. Vossepoel (TU Delft) Campus Golm, building 9, room 1.2210:00 - 11:00

**Abstract by Svetlana Dubinkina:**

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

more ›### Bayesian and Deterministic Spatio-Temporal Methods with Edge-Preserving Priors for Inverse Problems

Mirjeta Pasha, Tufts University, US Campus Golm, building 9, room 1.1012:13 - 13:15

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

more ›### Hurst index estimation for SPDEs

Pavel Kriz, Charles University HU Berlin (Rudower Chaussee 25, room: 3.008)13:15 - 17:45

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

more ›### IRTG Workshop - Presentation Skills for Science and Research (Part I - online)

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

more ›### IRTG Workshop - Presentation Skills for Science and Research (Part II )

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…

more ›