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.
 

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

 

 

Optimization and AI in Power Electronics

 

Optimization of Complex Heat Sink Structures

 

Simulation Analysis Based on CT Scan Data

 

AI-augmented Simulation

 

Advanced
Simulation
of Litz Wire
Systems

 

Publications AI-augmented Simulation