Data-Driven Modeling and Simulation

The simulation of complex multi-physical processes as additive manufacturing is extremely demanding and time-consuming using conventional numerical methods. Therefore, in engineering often only simplified  analytical or empirical models are used. Thanks to advances in machine learning, reliable empirical approaches can increasingly be obtained from big data. However, the generation of big data often requires complex and expensive sensor technology. In the presence of high speed and large thermal gradients it is sometimes simply not possible to reliably generate certain data, even with the best sensor technology at hand.

In our group we investigate to what extent simulation with neural networks on the one hand and data-based empirical modeling on the other hand can be combined in a symbiotic manner. The ultimate goal is the generation of reliable models for complex dynamical systems known as digital twins.