Seminars in 2020
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 ›