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 (Otto von Guericke University Magdeburg), Berend van Wachem (Otto von Guericke University Magdeburg)
Machine Learning for the Design and Control ofPower2X Processes with Application to MethanolSynthesis (mlP2X)
Achim Kienle (Otto von Guericke University Magdeburg), Sebastian Sager (Otto von Guericke University Magdeburg), Andreas Seidel-Morgenstern (Max Planck Institute for Dynamics of Complex Techncial Systems Magdeburg)
Collaborating projects:
Artificial intelligence on linked chemical engineering data
Artur Schweidtmann (TU Delft)