AI-augmented Simulation

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.
 

Simulation

• 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
   

Optimization

• 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