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

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

More details can be found here [read more]

Abstract: Fine-scale models based on high-dimensional differential equations (DEs) are available for many systems in science and engineering. In many cases, research focuses on effects which occur on a coarser scale instead of the fine one described by the DEs. As it is usually not feasible to… [read more]

Our group will participate in this year's  SIAM Annual Meeting (AN17) with a paper on: - Optimization of Random Systems Using Multi-Fidelity Models  More details about the conference can be found here: http://www.siam.org/meetings/an17/ [read more]

Our group will participate in the SIAM workshop on Parameter Space Dimension Reduction (DR17) with a paper on: - Probabilistic Coarse-Graining: from Molecular Dynamics to Stochastic PDEs [abstract] More details about the conference can be found here: http://www.siam.org/meetings/dr17/ [read more]

Abstract: This talk is concerned with the development of probabilistic reduced order models facilitating uncertainty quantification in partial differential equations with random and spatially varying coefficients. In particular, Poisson’s equation with a random, heterogeneous conductivity field is… [read more]

Abstract: This talk addresses the topic of uncertainty quantification in high-dimensional model-based Bayesian inverse problems with an application to linear elastostatic problems. For that purpose, a probabilistic mechanical model is proposed, recasting the traditional forward problem formulation… [read more]

Abstract: Accurate uncertainty quantification for model-based, large-scale inverse problems represents one of the fundamental challenges in the context of computational science and engineering. In this thesis, novel Bayesian methodologies for the quantification of parametric and model… [read more]

Very excited to participate in this highly multidisciplinary project that aims at developing new methods for  estimating local tumor infiltration and optimizing personalized treatment in glioma patients. More information can be found here. [read more]