Neuro-mechanistic Modeling

Mathematical models and their computer-aided simulation have played a fundamental role for scientific and technological progress for many decades. They often describe mechanistic relationships of a real-world system and are based on hypotheses about its functioning. But the complexity of such models is limited as they are designed by a modeler and strongly simplifying assumptions are required to make them manageable. On the contrary, models for data-driven learning, in particular artificial neural networks, are able to detect complex patterns in the data and exploit them for regression or classification tasks. But their highly specialised and complex decision rules are difficult to interpret and require enormous amounts of data, also because existing world knowledge can not or only hardly be integrated.

The research department on Neuro-mechanistic Modeling focuses on hybrid approaches that combine mechanistic and AI-based models. In our projects we will develop methods that leverage the high complexity of neural networks but also integrate interpretable mechanistic descriptions and thus combine the best of both worlds. In contrast to purely neural models, neuro-mechanistic models allow to integrate domain knowledge and they are more effective even if only moderate amounts of data are available as it is common, for instance, in life science applications. They are easier to interpret and generalise better to unknown inputs.