Artur M. Schweidtmann

51 papers Journal 49Unranked 1

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Environmental impacts prediction using graph neural networks on molecular graphs. Comput. Chem. Eng. Qinghe Gao, Lukas Schulze Balhorn, Alessandro Laera, Raoul Meys, Jonas Goßen, Jana M. Weber, Gregor Wernet, Artur M. Schweidtmann
2026	J	`jnl`	Graph neural networks for soft sensors: Learning from process topology and operational data. Comput. Chem. Eng. Maximilian F. Theisen, Gabrie M. H. Meesters, Artur M. Schweidtmann
2026	J	`jnl`	GraphRAG for Engineering Diagrams: ChatP&ID Enables LLM Interaction with P&IDs. CoRR Achmad Anggawirya Alimin, Artur M. Schweidtmann
2026	J	`jnl`	Grounding Large Language Models in Reaction Knowledge Graphs for Synthesis Retrieval. CoRR Olga Bunkova, Lorenzo Di Fruscia, Sophia Rupprecht, Artur M. Schweidtmann, Marcel J. T. Reinders, Jana M. Weber
2025	J	`jnl`	Accelerating process synthesis with reinforcement learning: Transfer learning from multi-fidelity simulations and variational autoencoders. Comput. Chem. Eng. Qinghe Gao, Haoyu Yang, Maximilian F. Theisen, Artur M. Schweidtmann
2025	J	`jnl`	ENFORCE: Exact Nonlinear Constrained Learning with Adaptive-depth Neural Projection. CoRR Giacomo Lastrucci, Artur M. Schweidtmann
2025	J	`jnl`	Multi-agent systems for chemical engineering: A review and perspective. CoRR Sophia Rupprecht, Qinghe Gao, Tanuj Karia, Artur M. Schweidtmann
2025	J	`jnl`	Picard-KKT-hPINN: Enforcing Nonlinear Enthalpy Balances for Physically Consistent Neural Networks. CoRR Giacomo Lastrucci, Tanuj Karia, Zoë Gromotka, Artur M. Schweidtmann
2025	J	`jnl`	Rule-based autocorrection of Piping and Instrumentation Diagrams (P&IDs) on graphs. CoRR Lukas Schulze Balhorn, Niels Seijsener, Kevin Dao, Minji Kim, Dominik P. Goldstein, Ge H. M. Driessen, Artur M. Schweidtmann
2025	J	`jnl`	Talking like Piping and Instrumentation Diagrams (P&IDs). CoRR Achmad Anggawirya Alimin, Dominik P. Goldstein, Lukas Schulze Balhorn, Artur M. Schweidtmann
2025	J	`jnl`	Text2Model: Generating dynamic chemical reactor models using large language models (LLMs). CoRR Sophia Rupprecht, Yassine Hounat, Monisha Kumar, Giacomo Lastrucci, Artur M. Schweidtmann
2025	J	`jnl`	Transferring Graph Neural Networks for Soft Sensor Modeling using Process Topologies. CoRR Maximilian F. Theisen, Gabrie M. H. Meesters, Artur M. Schweidtmann
2024	J	`jnl`	Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation. CoRR Yidong Zhao, Joao Tourais, Iain Pierce, Christian Nitsche, Thomas A. Treibel, Sebastian Weingärtner, Artur M. Schweidtmann, Qian Tao
2024	J	`jnl`	Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence. CoRR Lukas Schulze Balhorn, Kevin Degens, Artur M. Schweidtmann
2024	J	`jnl`	Machine learning in process systems engineering: Challenges and opportunities. Comput. Chem. Eng. Prodromos Daoutidis, Jay H. Lee, Srinivas Rangarajan, Leo Chiang, R. Bhushan Gopaluni, Artur M. Schweidtmann, Iiro Harjunkoski, Mehmet Mercangöz, Ali Mesbah, Fani Boukouvala, Fernando V. Lima, Ehecatl Antonio del Rio-Chanona, Christos Georgakis
2024	J	`jnl`	MachineLearnAthon: An Action-Oriented Machine Learning Didactic Concept. CoRR Michal Tkác, Jakub Sieber, Lara Kuhlmann, Matthias Brueggenolte, Alexandru Rinciog, Michael Henke, Artur M. Schweidtmann, Qinghe Gao, Maximilian F. Theisen, Radwa El Shawi
2024	J	`jnl`	Mixed-integer optimisation of graph neural networks for computer-aided molecular design. Comput. Chem. Eng. Tom McDonald, Calvin Tsay, Artur M. Schweidtmann, Neil Yorke-Smith
2023	J	`jnl`	Data augmentation for machine learning of chemical process flowsheets. CoRR Lukas Schulze Balhorn, Edwin Hirtreiter, Lynn Luderer, Artur M. Schweidtmann
2023	J	`jnl`	Data-driven Product-Process Optimization of N-isopropylacrylamide Microgel Flow-Synthesis. CoRR Luise F. Kaven, Artur M. Schweidtmann, Jan Keil, Jana Israel, Nadja Wolter, Alexander Mitsos
2023	J	`jnl`	Deep reinforcement learning for process design: Review and perspective. CoRR Qinghe Gao, Artur M. Schweidtmann
2023	J	`jnl`	Geometry optimization of a continuous millireactor via CFD and Bayesian optimization. Comput. Chem. Eng. Moritz J. Begall, Artur M. Schweidtmann, Adel Mhamdi, Alexander Mitsos
2023	J	`jnl`	Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids. Comput. Chem. Eng. Jan G. Rittig, Karim Ben Hicham, Artur M. Schweidtmann, Manuel Dahmen, Alexander Mitsos
2023	J	`jnl`	Learning from flowsheets: A generative transformer model for autocompletion of flowsheets. Comput. Chem. Eng. Gabriel Vogel, Lukas Schulze Balhorn, Artur M. Schweidtmann
2023	J	`jnl`	Mixed-Integer Optimisation of Graph Neural Networks for Computer-Aided Molecular Design. CoRR Tom McDonald, Calvin Tsay, Artur M. Schweidtmann, Neil Yorke-Smith
2023	J	`jnl`	Physical pooling functions in graph neural networks for molecular property prediction. Comput. Chem. Eng. Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber, Martin Grohe, Manuel Dahmen, Kai Leonhard, Alexander Mitsos
2023	J	`jnl`	Toward autocorrection of chemical process flowsheets using large language models. CoRR Lukas Schulze Balhorn, Marc Caballero, Artur M. Schweidtmann
2023	J	`jnl`	Transfer learning for process design with reinforcement learning. CoRR Qinghe Gao, Haoyu Yang, Shachi M. Shanbhag, Artur M. Schweidtmann
2023	J	`jnl`	What does ChatGPT know about natural science and engineering? CoRR Lukas Schulze Balhorn, Jana M. Weber, Stefan Buijsman, Julian Romeo Hildebrandt, Martina Ziefle, Artur M. Schweidtmann
2022	—	`conf`	Efficient Bayesian Uncertainty Estimation for nnU-Net. MICCAI (8) Yidong Zhao, Changchun Yang, Artur M. Schweidtmann, Qian Tao
2022	J	`jnl`	Efficient Bayesian Uncertainty Estimation for nnU-Net. CoRR Yidong Zhao, Changchun Yang, Artur M. Schweidtmann, Qian Tao
2022	J	`jnl`	Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks. CoRR Laura Stops, Roel Leenhouts, Qinghe Gao, Artur M. Schweidtmann
2022	J	`jnl`	Graph Machine Learning for Design of High-Octane Fuels. CoRR Jan G. Rittig, Martin Ritzert, Artur M. Schweidtmann, Stefanie Winkler, Jana M. Weber, Philipp Morsch, K. Alexander Heufer, Martin Grohe, Alexander Mitsos, Manuel Dahmen
2022	J	`jnl`	Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids. CoRR Jan G. Rittig, Karim Ben Hicham, Artur M. Schweidtmann, Manuel Dahmen, Alexander Mitsos
2022	J	`jnl`	Graph neural networks for the prediction of molecular structure-property relationships. CoRR Jan G. Rittig, Qinghe Gao, Manuel Dahmen, Alexander Mitsos, Artur M. Schweidtmann
2022	J	`jnl`	HybridML: Open source platform for hybrid modeling. Comput. Chem. Eng. Kilian Merkelbach, Artur M. Schweidtmann, Younes Müller, Patrick Schwoebel, Adel Mhamdi, Alexander Mitsos, Andreas Schuppert, Thomas Mrziglod, Sebastian Schneckener
2022	J	`jnl`	Learning from flowsheets: A generative transformer model for autocompletion of flowsheets. CoRR Gabriel Vogel, Lukas Schulze Balhorn, Artur M. Schweidtmann
2022	J	`jnl`	Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction. CoRR Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber, Martin Grohe, Manuel Dahmen, Kai Leonhard, Alexander Mitsos
2022	J	`jnl`	SFILES 2.0: An extended text-based flowsheet representation. CoRR Gabriel Vogel, Lukas Schulze Balhorn, Edwin Hirtreiter, Artur M. Schweidtmann
2022	J	`jnl`	Towards automatic generation of Piping and Instrumentation Diagrams (P&IDs) with Artificial Intelligence. CoRR Edwin Hirtreiter, Lukas Schulze Balhorn, Artur M. Schweidtmann
2021	J	`jnl`	Deterministic global optimization with Gaussian processes embedded. Math. Program. Comput. Artur M. Schweidtmann, Dominik Bongartz, Daniel Grothe, Tim Kerkenhoff, Xiaopeng Lin, Jaromil Najman, Alexander Mitsos
2021	—	`—`	Global optimization of processes through machine learning. Artur M. Schweidtmann
2021	J	`jnl`	Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb. Frontiers Bioinform. Ulf W. Liebal, Sebastian Köbbing, Linus Netze, Artur M. Schweidtmann, Alexander Mitsos, Lars M. Blank
2020	J	`jnl`	Deterministic global superstructure-based optimization of an organic Rankine cycle. Comput. Chem. Eng. Wolfgang R. Huster, Artur M. Schweidtmann, Jannik T. Lüthje, Alexander Mitsos
2020	J	`jnl`	Global Optimization of Gaussian processes. CoRR Artur M. Schweidtmann, Dominik Bongartz, Daniel Grothe, Tim Kerkenhoff, Xiaopeng Lin, Jaromil Najman, Alexander Mitsos
2020	J	`jnl`	Wavelet-based grid-adaptation for nonlinear scheduling subject to time-variable electricity prices. Comput. Chem. Eng. Pascal Schäfer, Artur M. Schweidtmann, Philipp H. A. Lenz, Hannah M. C. Markgraf, Alexander Mitsos
2019	J	`jnl`	Deterministic Global Optimization with Artificial Neural Networks Embedded. J. Optim. Theory Appl. Artur M. Schweidtmann, Alexander Mitsos
2019	J	`jnl`	Deterministic global process optimization: Accurate (single-species) properties via artificial neural networks. Comput. Chem. Eng. Artur M. Schweidtmann, Wolfgang R. Huster, Jannik T. Lüthje, Alexander Mitsos
2019	J	`jnl`	Model-based bidding strategies on the primary balancing market for energy-intense processes. Comput. Chem. Eng. Pascal Schäfer, Hermann Graf Westerholt, Artur M. Schweidtmann, Svetlina Ilieva, Alexander Mitsos
2018	J	`jnl`	Correction to: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm. J. Glob. Optim. Eric Bradford, Artur M. Schweidtmann, Alexei A. Lapkin
2018	J	`jnl`	Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes. Comput. Chem. Eng. Eric Bradford, Artur M. Schweidtmann, Dongda Zhang, Keju Jing, Ehecatl Antonio del Rio-Chanona
2018	J	`jnl`	Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm. J. Glob. Optim. Eric Bradford, Artur M. Schweidtmann, Alexei A. Lapkin