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Found 6 papers, 1 papers with code
MachineLearnAthon: An Action-Oriented Machine Learning Didactic Concept
Machine Learning (ML) techniques are encountered nowadays across disciplines, from social sciences, through natural sciences to engineering.
Deep reinforcement learning for process design: Review and perspective
The transformation towards renewable energy and feedstock supply in the chemical industry requires new conceptual process design approaches.
Transfer learning for process design with reinforcement learning
We thus propose to utilize transfer learning for process design with RL in combination with rigorous simulation methods.
Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks
In particular, the graph neural networks are implemented within the agent architecture to process the states and make decisions.
Graph neural networks for the prediction of molecular structure-property relationships
Molecular property prediction is of crucial importance in many disciplines such as drug discovery, molecular biology, or material and process design.
Modeling Category-Selective Cortical Regions with Topographic Variational Autoencoders
Category-selectivity in the brain describes the observation that certain spatially localized areas of the cerebral cortex tend to respond robustly and selectively to stimuli from specific limited categories.
