Artur M. Schweidtmann

Latest

• Generative artificial intelligence in chemical engineering
• Machine learning in process systems engineering: Challenges and opportunities
• Data-driven Product-Process Optimization of N-isopropylacrylamide Microgel Flow-Synthesis
• A review and perspective on hybrid modeling methodologies
• Empirical assessment of ChatGPT’s answering capabilities in natural science and engineering
• Deep reinforcement learning for process design: Review and perspective
• An Educational Workshop for Effective PSE Course Development
• Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning
• Graph machine learning for design of high-octane fuels
• Toward automatic generation of control structures for process flow diagrams with large language models
• Digitization of chemical process flow diagrams using deep convolutional neural networks
• Geometry optimization of a continuous millireactor via CFD and Bayesian optimization
• Learning from flowsheets: A generative transformer model for autocompletion of flowsheets
• SFILES 2.0: an extended text-based flowsheet representation
• Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids
• Physical pooling functions in graph neural networks for molecular property prediction