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Qinghe Gao
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Flowsheet generation through hierarchical reinforcement learning and graph neural networks
L Stops, R Leenhouts, Q Gao, AM Schweidtmann
AIChE Journal 69 (1), e17938, 2023
38*2023
Deep reinforcement learning for process design: Review and perspective
Q Gao, AM Schweidtmann
Current Opinion in Chemical Engineering 44, 101012, 2024
192024
Graph Neural Networks for the Prediction of Molecular Structure–Property Relationships
JG Rittig, Q Gao, M Dahmen, A Mitsos, AM Schweidtmann
182023
Modeling category-selective cortical regions with topographic variational autoencoders
TA Keller, Q Gao, M Welling
arXiv preprint arXiv:2110.13911, 2021
182021
Transfer learning for process design with reinforcement learning
Q Gao, H Yang, SM Shanbhag, AM Schweidtmann
Computer Aided Chemical Engineering 52, 2005-2010, 2023
102023
Flowsheet recognition using deep convolutional neural networks
LS Balhorn, Q Gao, D Goldstein, AM Schweidtmann
Computer Aided Chemical Engineering 49, 1567-1572, 2022
72022
Self-supervised graph neural networks for polymer property prediction
Q Gao, T Dukker, AM Schweidtmann, JM Weber
Molecular Systems Design & Engineering 9 (11), 1130-1143, 2024
12024
Teaching machine learning to programming novices: an action-oriented didactic concept
M Tkáč, J Sieber, A Meyer, L Kuhlmann, M Brueggenolte, A Rinciog, ...
IDIMT-2024: Changes to ICT, Management, and Business Processes through AI, 2024
2024
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