Aktuelles

at the "Big Data Challenges for Predictive Modeling of Complex Systems" Workshop at the University of Hong Kong

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

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 on: - Beyond Black-boxes in Model-based Bayesian Inverse Problems - A Bayesian Coarse-graining Approach to the Solution of Stochastic Partial Differential Equations - Incorporating…

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News

The paper "Predictive Collective Variable Discovery with Deep Bayesian Models" has been accepted for publication.

Keynote talk on "Physics-aware, coarse-grained models for high-dimensional, uncertainty quantification"

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

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

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

Our group will participate in WCCM 2018 with 3 papers

Christoph Wellnhofer presents his M.Sc. thesis on "A Bayesian Multi-Fidelity Approach for Inverse Problems"

Our group will participate in this year's SIAM Uncertainty Quantification Conference (UQ18) with four papers

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

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