Modeling + Numerics + Machine Learning
Engineers develop models based on physical considerations and observational data to design, monitor and control infrastructures and products. Complex models can only be solved numerically with the aid of computers. Tailored machine learning approaches help us to link the information from experiments, sensing and simulations to build next generation models: digital twins.
Parameter identification and model update
Engineers often have to deal with incomplete observations. Given such incomplete information, what is the stress field inside a steel girder? How does the flow field inside a river look like? Inverse modeling and parameter identification are our strategies to relate sparse information to what remains hidden to the human eye. If the computational model that has been used to design a product, process or infrastructure, becomes outdated due to ageing or damage effects, efficient model updating strategies need to be employed.
Multi-scale modeling
It is understood that the composition and structure of materials at smaller scales determines their behavior on the component scale that is relevant for designers. We develop data-driven models to exploit this understanding for the optimization of materials in virtual design loops and for their monitoring during service life.
Additive manufacturing & multi-physics
Increased flexibility for designers is what made additive manufacturing popular. This flexibility is mostly due to the large amount of process parameters, which make process planning a complicated endeavor. With sophisticated computational methods we aim to increase this flexibility beyond what is known today, e.g. towards functionally graded materials.