Process Modeling and Simulation

We develop physics-based and data-driven computational models as well as corresponding surrogate models for complex, continuous processes, for example for solid-liquid phase-change, processes in contact films or shallow flows.

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Methods for Predictive Simulations

Model-based development and decision support require an in-depth understanding of the predictive quality of the models used. To control this, we for instance use Bayesian methods for parameter estimation and model selection as well as probabilistic simulations.

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Federated Model and Data Architectures

Modeling of a complete system requires the description of its geometry, functionality and relevant processes, as well as its interaction with the ambient environment. We are working on integrating the silo solutions available for the individual levels.

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