Skip to content
Technical University of Munich

News

Zhiyi He presents her M.Sc. Thesis on "Bayesian Generative Adversarial Networks for Medical Image Synthesis: A Comparative Study"

Abstract: Machine Learning (ML) is widely utilized in various fields to solve problems and assist research. In the field of medical image synthesis, Deep Learning (DL) shows great potential. Recently, different kinds of frameworks of Generative Adversarial Network (GAN) have been developed.…

Simon Jarvers presents his B.Sc. thesis on "Machine Learning of ODE with Gaussian Processes"

Abstract: Gaussian processes are a well-studied stochastic machine learning method that has proven to be useful in many areas of application. They are flexible due to their non- parametric approach and are able to quantify the uncertainty of their predictions. This work describes the basic…

Paper on "Physics-aware, probabilistic model order reduction with guaranteed stability" " accepted for ICLR 2021

Our paper on "Physics-aware, probabilistic model order reduction with guaranteed stability" has been accepted to #ICLR2021.  This year's acceptance rate was 29%. Congratulations to Sebastian Kaltenbach for putting in all the hard work! More details can be found…

New work on "A probabilistic generative model for semi-supervised training of coarse-grained surrogates and enforcing physical constraints through virtual observables"

More details here Github repository here

Tobias Pielok presents his M.Sc. thesis on "Residual enhanced Probabilistic Koopman-based Representation Learning

Abstract:  In this thesis, we try to find a probabilistic Koopman-based representation for dynamical systems. Therefore we apply the framework of [Pan et al 2019], where the author successfully found such representation as a residual improvement of the powerful Dynamic Mode Decomposition technique.…

Tim Beyer presents his B.Sc. thesis on "Neural Ordinary Differential Equations for Physical Problems"

Abstract: Accurate modeling of physical systems is of great importance to engineers. In this thesis, we construct novel machine learning models based on the Neural ODE approach in Chen et al. (2018) and compare them to existing architectures. The evaluation is performed on multiple physical systems,…

Zhiyi He presents her Semester Thesis on "Magnetic Resonance Images Reconstruction Using Generative Adversarial Networks and Uncertainty Analysis" on 08.05.2020

New work on "Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems"

More details here Github repository here

Christoph Wellnhofer presents his M.Sc. thesis on "A Bayesian Multi-Fidelity Approach for Inverse Problems"

Abstract: Inverse problems are ubiquitous in the engineering domain and often rely on computationally expensive forward models. For applications with societal or economical impact it is of major importance to quantify the uncertainties associated with the simulation results. A Bayesian formulation…

Our group will participate in this year's SIAM Uncertainty Quantification Conference (UQ18) with four papers

Our group will participate in this year's SIAM Uncertainty Quantification Conference (UQ18) with four papers on: - Beyond Black-boxes in Model-based Bayesian Inverse Problems - A Bayesian Coarse-graining Approach to the Solution of Stochastic Partial Differential Equations - Incorporating…

To top