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More details here Github repository here [read more]

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… [read more]

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]