Hybrid Modeling for Data-enhanced Multiobjective Optimization of Multibody Systems

In almost all technical systems, we want to optimize several conflicting criteria simultaneously such as, the minimization of the energy consumption, the construction cost, or the system complexity. Instead of a single optimum, we thus need to calculate the Pareto front of optimal compromise solutions (cf. the Figure below).

• Problem: Model-based multiobjective optimization is very expensive
• Solution: Use efficient and accurate surrogate models

• White-box models (e.g. differential equations) with a high degree of physical knowledge are often too complex and/or too inaccurate
• Black-box models (generated e.g. by machine learning methods such as decision trees or neural networks) are often not explainable and cost-intensive (data collection)

Hybrid modeling as a combination of physics-based white-box models and data-driven black-box models, e.g. in the form of a discrepancy model

• Experience with coupling FMD software and AI model execution capabilities (e.g., co-simulation, subroutines)
• Other interesting model classes and applications

• Multicriteria optimization of dynamical systems
• Physical and hybrid modeling of dynamical systems