Artur M. Schweidtmann

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 36

Top Topics

Global Optimization

Genetic Algorithm

Gaussian Processes

Top Venues

Comput. Chem. Eng.

J. Glob. Optim.

J. Optim. Theory Appl.

Publications

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
Machine learning in process systems engineering: Challenges and opportunities. Comput. Chem. Eng. 181 (2024)
Tom McDonald, Calvin Tsay, Artur M. Schweidtmann, Neil Yorke-Smith
Mixed-integer optimisation of graph neural networks for computer-aided molecular design. Comput. Chem. Eng. 185 (2024)
Michal Tkác, Jakub Sieber, Lara Kuhlmann, Matthias Brueggenolte, Alexandru Rinciog, Michael Henke, Artur M. Schweidtmann, Qinghe Gao, Maximilian F. Theisen, Radwa El Shawi
MachineLearnAthon: An Action-Oriented Machine Learning Didactic Concept. CoRR (2024)
Yidong Zhao, Joao Tourais, Iain Pierce, Christian Nitsche, Thomas A. Treibel, Sebastian Weingärtner, Artur M. Schweidtmann, Qian Tao
Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation. CoRR (2024)
Lukas Schulze Balhorn, Jana M. Weber, Stefan Buijsman, Julian Romeo Hildebrandt, Martina Ziefle, Artur M. Schweidtmann
What does ChatGPT know about natural science and engineering? CoRR (2023)
Gabriel Vogel, Lukas Schulze Balhorn, Artur M. Schweidtmann
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets. Comput. Chem. Eng. 171 (2023)
Moritz J. Begall, Artur M. Schweidtmann, Adel Mhamdi, Alexander Mitsos
Geometry optimization of a continuous millireactor via CFD and Bayesian optimization. Comput. Chem. Eng. 171 (2023)
Lukas Schulze Balhorn, Marc Caballero, Artur M. Schweidtmann
Toward autocorrection of chemical process flowsheets using large language models. CoRR (2023)
Jan G. Rittig, Karim Ben Hicham, Artur M. Schweidtmann, Manuel Dahmen, Alexander Mitsos
Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids. Comput. Chem. Eng. 171 (2023)
Tom McDonald, Calvin Tsay, Artur M. Schweidtmann, Neil Yorke-Smith
Mixed-Integer Optimisation of Graph Neural Networks for Computer-Aided Molecular Design. CoRR (2023)
Luise F. Kaven, Artur M. Schweidtmann, Jan Keil, Jana Israel, Nadja Wolter, Alexander Mitsos
Data-driven Product-Process Optimization of N-isopropylacrylamide Microgel Flow-Synthesis. CoRR (2023)
Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber, Martin Grohe, Manuel Dahmen, Kai Leonhard, Alexander Mitsos
Physical pooling functions in graph neural networks for molecular property prediction. Comput. Chem. Eng. 172 (2023)
Qinghe Gao, Haoyu Yang, Shachi M. Shanbhag, Artur M. Schweidtmann
Transfer learning for process design with reinforcement learning. CoRR (2023)
Qinghe Gao, Artur M. Schweidtmann
Deep reinforcement learning for process design: Review and perspective. CoRR (2023)
Lukas Schulze Balhorn, Edwin Hirtreiter, Lynn Luderer, Artur M. Schweidtmann
Data augmentation for machine learning of chemical process flowsheets. CoRR (2023)
Laura Stops, Roel Leenhouts, Qinghe Gao, Artur M. Schweidtmann
Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks. CoRR (2022)
Gabriel Vogel, Lukas Schulze Balhorn, Edwin Hirtreiter, Artur M. Schweidtmann
SFILES 2.0: An extended text-based flowsheet representation. CoRR (2022)
Yidong Zhao, Changchun Yang, Artur M. Schweidtmann, Qian Tao
Efficient Bayesian Uncertainty Estimation for nnU-Net. CoRR (2022)
Gabriel Vogel, Lukas Schulze Balhorn, Artur M. Schweidtmann
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets. CoRR (2022)
Edwin Hirtreiter, Lukas Schulze Balhorn, Artur M. Schweidtmann
Towards automatic generation of Piping and Instrumentation Diagrams (P&IDs) with Artificial Intelligence. CoRR (2022)
Jan G. Rittig, Karim Ben Hicham, Artur M. Schweidtmann, Manuel Dahmen, Alexander Mitsos
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids. CoRR (2022)
Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber, Martin Grohe, Manuel Dahmen, Kai Leonhard, Alexander Mitsos
Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction. CoRR (2022)
Kilian Merkelbach, Artur M. Schweidtmann, Younes Müller, Patrick Schwoebel, Adel Mhamdi, Alexander Mitsos, Andreas Schuppert, Thomas Mrziglod, Sebastian Schneckener
HybridML: Open source platform for hybrid modeling. Comput. Chem. Eng. 160 (2022)
Jan G. Rittig, Martin Ritzert, Artur M. Schweidtmann, Stefanie Winkler, Jana M. Weber, Philipp Morsch, K. Alexander Heufer, Martin Grohe, Alexander Mitsos, Manuel Dahmen
Graph Machine Learning for Design of High-Octane Fuels. CoRR (2022)
Jan G. Rittig, Qinghe Gao, Manuel Dahmen, Alexander Mitsos, Artur M. Schweidtmann
Graph neural networks for the prediction of molecular structure-property relationships. CoRR (2022)
Artur M. Schweidtmann, Dominik Bongartz, Daniel Grothe, Tim Kerkenhoff, Xiaopeng Lin, Jaromil Najman, Alexander Mitsos
Deterministic global optimization with Gaussian processes embedded. Math. Program. Comput. 13 (3) (2021)
Ulf W. Liebal, Sebastian Köbbing, Linus Netze, Artur M. Schweidtmann, Alexander Mitsos, Lars M. Blank
Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb. Frontiers Bioinform. 1 (2021)
Pascal Schäfer, Artur M. Schweidtmann, Philipp H. A. Lenz, Hannah M. C. Markgraf, Alexander Mitsos
Wavelet-based grid-adaptation for nonlinear scheduling subject to time-variable electricity prices. Comput. Chem. Eng. 132 (2020)
Artur M. Schweidtmann, Dominik Bongartz, Daniel Grothe, Tim Kerkenhoff, Xiaopeng Lin, Jaromil Najman, Alexander Mitsos
Global Optimization of Gaussian processes. CoRR (2020)
Wolfgang R. Huster, Artur M. Schweidtmann, Jannik T. Lüthje, Alexander Mitsos
Deterministic global superstructure-based optimization of an organic Rankine cycle. Comput. Chem. Eng. 141 (2020)
Artur M. Schweidtmann, Wolfgang R. Huster, Jannik T. Lüthje, Alexander Mitsos
Deterministic global process optimization: Accurate (single-species) properties via artificial neural networks. Comput. Chem. Eng. 121 (2019)
Pascal Schäfer, Hermann Graf Westerholt, Artur M. Schweidtmann, Svetlina Ilieva, Alexander Mitsos
Model-based bidding strategies on the primary balancing market for energy-intense processes. Comput. Chem. Eng. 120 (2019)
Artur M. Schweidtmann, Alexander Mitsos
Deterministic Global Optimization with Artificial Neural Networks Embedded. J. Optim. Theory Appl. 180 (3) (2019)
Eric Bradford, Artur M. Schweidtmann, Alexei A. Lapkin
Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm. J. Glob. Optim. 71 (2) (2018)
Eric Bradford, Artur M. Schweidtmann, Alexei A. Lapkin
Correction to: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm. J. Glob. Optim. 71 (2) (2018)
Eric Bradford, Artur M. Schweidtmann, Dongda Zhang, Keju Jing, Ehecatl Antonio del Rio-Chanona
Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes. Comput. Chem. Eng. 118 (2018)