| Automated synthesis of steady-state continuous processes using reinforcement learning Q Göttl, DG Grimm, J Burger Frontiers of Chemical Science and Engineering, 1-15, 2022 | 24 | 2022 |
| Automated flowsheet synthesis using hierarchical reinforcement learning: proof of concept Q Göttl, Y Tönges, DG Grimm, J Burger Chemie Ingenieur Technik 93 (12), 2010-2018, 2021 | 18 | 2021 |
| Using reinforcement learning in a game-like setup for automated process synthesis without prior process knowledge Q Göttl, DG Grimm, J Burger Computer Aided Chemical Engineering 49, 1555-1560, 2022 | 5 | 2022 |
| Automated Process Synthesis Using Reinforcement Learning Q Göttl, D Grimm, J Burger Computer Aided Chemical Engineering 50, 209-214, 2021 | 4 | 2021 |
| Policy-based self-competition for planning problems J Pirnay, Q Göttl, J Burger, DG Grimm arXiv preprint arXiv:2306.04403, 2023 | 3 | 2023 |
| Convex Envelope Method for determining liquid multi-phase equilibria in systems with arbitrary number of components Q Göttl, J Pirnay, DG Grimm, J Burger Computers & Chemical Engineering 177, 108321, 2023 | 2 | 2023 |
| Deep reinforcement learning uncovers processes for separating azeotropic mixtures without prior knowledge Q Göttl, J Pirnay, J Burger, DG Grimm arXiv preprint arXiv:2310.06415, 2023 | 1 | 2023 |
| Multiple solutions when fitting excess Gibbs energy models and implications for process simulation D Vasiliu, Q Göttl, S Bröcker, J Burger Chemie Ingenieur Technik 93 (3), 490-496, 2021 | 1 | 2021 |
| Automated Flowsheet Synthesis via Reinforcement Learning and piece-wise linear thermodynamic Models Q Göttl Technische Universität München, 2024 | | 2024 |
| Automatisierte Fließbildsynthese durch Reinforcement Learning Q Göttl, DG Grimm, J Burger Chemie Ingenieur Technik 92 (9), 1240-1240, 2020 | | 2020 |
| Deep Reinforcement Learning Enables Conceptual Design of Processes for Separating Azeotropic Mixtures Without Prior Knowledge Q Göttl, J Pirnay, J Burger, DG Grimm Available at SSRN 4776784, 0 | | |
