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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…

Prof. N. Zabaras is awarded the title of "TUM Ambassador"

Prof. Zabaras was hosted by our group from 2014-2017 as TUM-IAS Hans Fischer Senior Fellow.

Prof. Koutsourelakis is one of the keynote speakers during Speedup 2017

The title of the talk was "Physics-conversant machine learning: from molecular dynamics to stochastic PDEs". More details can be found here.

Maximilian Koschade presents Hands-on Tutorial on "Bayesian Multi-Level Monte Carlo" during Speedup 2017

More details can be found here

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