Optimization and Machine Learning
About
In the Optimization and Machine Learning department, we apply and develop numerical optimization methods and algorithms for the design and operation of complex energy systems. Important challenges in the optimization of such systems are nonlinearity (in particular nonconvexity), dynamics, multiple scales, uncertainty, and discrete decisions. Our expertise covers (deterministic) global optimization, stochastic programming, bilevel optimization, and dynamic optimization.
Research Topics
A particular focus of our work is the combination of numerical optimization and machine learning (ML). Tailoring optimization methods allow us to solve design and operation problems with artificial neural networks embedded into hybrid system models. To tackle large-scale problems, we develop methods for ML-based surrogate modeling and algorithms for parallel and high-performance computing architectures
