Parameter analysis and optimization are common tasks in engineering applications. SCAI develops DesParO, a machine learning software package for intuitive exploration, automatic analysis, and optimization of parametrized problems. DesParO can be coupled with simulation packages or used for measurement data. It minimizes the number of simulations needed for building up a model adaptively. Fraunhofer SCAI applies this approach in particular for energy networks.
Networks for transport of gas, electricity, water, steam or chemicals are essential for modern economy. The Energiewende (energy turnaround) requires optimization of energy flows in order to reduce consumption of non-renewable energy sources. The transformation of our energy backbone needs modeling, simulation and optimization of the corresponding networks.
The different energy networks can be mathematically modeled in a very similar way. Their numerical simulation can be based on the same numerical kernels. Our MultiphYsical NeTwork Simulation framework (MYNTS) is suited for all energy networks. MYNTS uses HPC technology. For parameter analysis and optimization, MYNTS provides an interface to DesParO.