AI-augmented Simulation

Enhancement of Simulation Methods by Artificial Intelligence and Machine Learning

The research group AI-augmented Simulation combines a wide range of physical-based simulation approaches and software tools with both mathematically advanced and efficient algorithms and AI frameworks to model and optimize power electronic devices and systems.


■  Coupled electrical, thermal and mechanical simulations in the domain of micro- and power electronic devices and energy systems

■  Implementation of customized software packages to increase efficiency, performance and functionality of standard software products

■  Simulation of electrical components based on CAD data from 3D CT scans

Artificial Intelligence

■  Clustering unknown data sets using unsupervised learning algorithms

■  Recurrent neural networks for analyzing and predicting time series

■  Convolutional neural networks for signal and image processing


■  Topology optimization of inductive components using gradient-based methods such as SIMP

■  Optimization of electrical networks and systems with genetic algorithms

■  Sensitivity analysis and visualization of big data


Optimization and AI in Power Electronics


Optimization of Complex Heat Sink Structures


Simulation Analysis Based on CT Scan Data


AI-augmented Simulation


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Publications AI-augmented Simulation