Multicriteria machine learning – efficiency, robustness, interactivity and system knowledge


Starting in September 2022, the focus of this BMBF funded AI junior research group “Multicriteria Machine Learning – Efficiency, Robustness, Inter-activity and System Knowledge” is on the development of multiobjective training algorithms for deep learning. Deep neural networks are of utmost importance in many areas of application. However, the consideration of multiple training criteria as well as system knowledge requires further investigation and has great potential for further improvements. In particular, we perform basic research on

  • The development of efficient optimization algorithms for training neural networks regarding multiple conflicting objective functions
  • Interactive learning and adaptation of deep neural networks using techniques from multiobjective optimization
  • Consideration of system knowledge, e.g., in the form of conservation laws or differential equations

Key Facts

Project type:
Project duration:
09/2022 - 08/2025
Funded by:
New AI Junior Research Group on Multicriteria Machine Learning
Current research projects

More Information

Principal Investigators

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Jun.-Prof. Dr. Sebastian Peitz

Data Science for Engineering

About the person