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Searched for: author%3A%22Schweidtmann%2C+A.M.%22
(1 - 20 of 27)
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A review and perspective on hybrid modeling methodologies
Empirical assessment of ChatGPT’s answering capabilities in natural science and engineering
Mixed-integer optimisation of graph neural networks for computer-aided molecular design
Deep reinforcement learning for process design: Review and perspective
Machine learning in process systems engineering: Challenges and opportunities
Data-driven product-process optimization of N-isopropylacrylamide microgel flow-synthesis
Physical pooling functions in graph neural networks for molecular property prediction
Digitization of chemical process flow diagrams using deep convolutional neural networks
Toward automatic generation of control structures for process flow diagrams with large language models
Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids
Geometry optimization of a continuous millireactor via CFD and Bayesian optimization
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets
Molecular Design of Fuels for Maximum Spark-Ignition Engine Efficiency by Combining Predictive Thermodynamics and Machine Learning
SFILES 2.0: an extended text-based flowsheet representation
Data augmentation for machine learning of chemical process flowsheets
Transfer learning for process design with reinforcement learning
An Educational Workshop for Effective PSE Course Development
Flowsheet generation through hierarchical reinforcement learning and graph neural networks
Multi-objective Bayesian optimisation of a two-step synthesis of p-cymene from crude sulphate turpentine
Graph machine learning for design of high-octane fuels
Searched for: author%3A%22Schweidtmann%2C+A.M.%22
(1 - 20 of 27)
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2
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