Research projects



ML-PRE: Machine Learning for Explainable Roundtrip Polymer Reaction Engineering
Sabine Beuermann (TU Clausthal), Jelena Fiosina (TU Clausthal)

Kernel Methods for Confidence Regions in Optimal Experimental Design and Parameter Estimation
Erik Esche (TU Berlin), Michael Bortz (FHI f. Techno- und Wirtschaftsmathematik (ITWM))

Reinforcement Learning for Automated Flowsheet Synthesis of Steady-State Processes

Jakob Burger (TUM), Dominik Grimm (Weihenstephan-Triesdorf HSWT)

Translating Thermodynamic Knowledge to Computers
Hans Hasse (TU Kaiserslautern), Sophie Burkhardt (TU Kaiserslautern), Heike Leitte (TU Kaiserslautern)

Hybrid Physics-Neural Network Soft Sensors for Dynamic Operation of Liqu.-Liqu. Sep. Processes
Manuel Dahmen (FZ Juelich), Andreas Jupke (RWTH Aachen)

Machine learning for design of chemical engineering unit operations
 Pascal Friederich (KIT), Alexander Stroh (KIT), Bradley Ladewig (KIT)

Graph-based Generative Machine Learning for Optimal Molecular Design
Martin Grohe (RWTH Aachen), Alexander Mitsos (RWTH Aachen)

Machine Learning and Optimal Experimental Design for Thermodynamic Property Modeling

Roland Herzog (Universitaet Heidelberg), Markus Richter (TU Chemnitz)

Safe Reinforcement Learning for Start-up and Operation of Chemical Processes

Jens-Uwe Repke (TU Berlin), Sergio Lucia Gil (TU Dortmund)

Improving Simulations of Large-Scale Dense Particle-Laden Flows with Machine Learning:
A Genetic Programming Approach

Sanaz Mostaghim (Magdeburg OVGU), Berend van Wachem (Magdeburg OVGU)


Collaborating projects:

Artificial intelligence on linked chemical engineering data

Artur Schweidtmann (TU Delft)