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… [read more]

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]