Zum Inhalt springen
Technische Universität München

News

Talk on "Coarse-grained variable and model discovery through latent variables and neural nets"

at the CECAM Workshop on "New frontiers in particle-based multiscale and coarse-grained modeling"

The paper "Physics-constrained, data-driven discovery of coarse-grained dynamics" has been accepted for publication.

More details here

The paper "Beyond black-boxes in Bayesian inverse problems and model validation: applications in solid mechanics of elastography" has been accepted for publication

Mode details here

Our group will participate in WCCM 2018 with 3 papers

More details 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…

New paper on "Bayesian model and dimension reduction for uncertainty propagation: applications in random media"

More details here

The paper "Fast uncertainty quantification of activation sequences in patient‐specific cardiac electrophysiology meeting clinical time constraints" has been accepted for publication.

More details here

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.

To top