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]

...and the possibility of moving to a a blind-peer-review-based system [read more]

Our group will participate with three papers in this year's SIAM CSE 2017, namely: - Bayesian, Multi-Fidelity, Optimization under Uncertainty [slides] - Bayesian Coarse-Graining in Atomistic Simulations: Adaptive Identification of the Dimensionality and Salient Features [slides] - Probabilistic,… [read more]

Abstract: This presentation discusses the problem of optimization under uncertainty in a high-dimensional, numerically expensive setting and it's solution with limited computational resources. To this end we show how the problem of stochastic optimization can elegantly be rephrased as one of… [read more]

Please visit: http://www.tum-ias.de/bigdata2017/registration.html [read more]

Abstract: The present thesis is concerned with the topic of stochastic variational inference and its application to model-based Bayesian inverse problems. Variational inference, like Markov chain Monte Carlo (MCMC), is a method to evaluate intractable, complex prob- ability distributions. In… [read more]