Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes. - Eric Bradford; Artur M. Schweidtmann; Dongda Zhang; Keju Jing; Ehecatl Antonio del Rio-Chanona (Comput. Chem. Eng., 2018)

Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm. - Eric Bradford; Artur M. Schweidtmann; Alexei A. Lapkin (J. Glob. Optim., 2018)

Correction to: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm. - Eric Bradford; Artur M. Schweidtmann; Alexei A. Lapkin (J. Glob. Optim., 2018)

Model-based bidding strategies on the primary balancing market for energy-intense processes. - Pascal Schäfer; Hermann Graf Westerholt; Artur M. Schweidtmann; Svetlina Ilieva; Alexander Mitsos (Comput. Chem. Eng., 2019)

Deterministic global process optimization: Accurate (single-species) properties via artificial neural networks. - Artur M. Schweidtmann; Wolfgang R. Huster; Jannik T. Lüthje; Alexander Mitsos (Comput. Chem. Eng., 2019)

Deterministic Global Optimization with Artificial Neural Networks Embedded. - Artur M. Schweidtmann; Alexander Mitsos (J. Optim. Theory Appl., 2019)

Deterministic global superstructure-based optimization of an organic Rankine cycle. - Wolfgang R. Huster; Artur M. Schweidtmann; Jannik T. Lüthje; Alexander Mitsos (Comput. Chem. Eng., 2020)

Wavelet-based grid-adaptation for nonlinear scheduling subject to time-variable electricity prices. - Pascal Schäfer; Artur M. Schweidtmann; Philipp H. A. Lenz; Hannah M. C. Markgraf; Alexander Mitsos (Comput. Chem. Eng., 2020)

Global Optimization of Gaussian processes. - Artur M. Schweidtmann; Dominik Bongartz; Daniel Grothe; Tim Kerkenhoff; Xiaopeng Lin; Jaromil Najman; Alexander Mitsos (CoRR, 2020)

Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb. - Ulf W. Liebal; Sebastian Köbbing; Linus Netze; Artur M. Schweidtmann; Alexander Mitsos; Lars M. Blank (Frontiers Bioinform., 2021)

Deterministic global optimization with Gaussian processes embedded. - Artur M. Schweidtmann; Dominik Bongartz; Daniel Grothe; Tim Kerkenhoff; Xiaopeng Lin; Jaromil Najman; Alexander Mitsos (Math. Program. Comput., 2021)

Global optimization of processes through machine learning. - Artur M. Schweidtmann (2021)

Efficient Bayesian Uncertainty Estimation for nnU-Net. - Yidong Zhao; Changchun Yang; Artur M. Schweidtmann; Qian Tao (MICCAI (8), 2022)

HybridML: Open source platform for hybrid modeling. - Kilian Merkelbach; Artur M. Schweidtmann; Younes Müller; Patrick Schwoebel; Adel Mhamdi; Alexander Mitsos; Andreas Schuppert; Thomas Mrziglod; Sebastian Schneckener (Comput. Chem. Eng., 2022)

Graph Machine Learning for Design of High-Octane Fuels. - Jan G. Rittig; Martin Ritzert; Artur M. Schweidtmann; Stefanie Winkler; Jana M. Weber; Philipp Morsch; K. Alexander Heufer; Martin Grohe; Alexander Mitsos; Manuel Dahmen (CoRR, 2022)

Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks. - Laura Stops; Roel Leenhouts; Qinghe Gao; Artur M. Schweidtmann (CoRR, 2022)

SFILES 2.0: An extended text-based flowsheet representation. - Gabriel Vogel; Lukas Schulze Balhorn; Edwin Hirtreiter; Artur M. Schweidtmann (CoRR, 2022)

Graph neural networks for the prediction of molecular structure-property relationships. - Jan G. Rittig; Qinghe Gao; Manuel Dahmen; Alexander Mitsos; Artur M. Schweidtmann (CoRR, 2022)

Towards automatic generation of Piping and Instrumentation Diagrams (P&IDs) with Artificial Intelligence. - Edwin Hirtreiter; Lukas Schulze Balhorn; Artur M. Schweidtmann (CoRR, 2022)

Transfer learning for process design with reinforcement learning. - Qinghe Gao; Haoyu Yang; Shachi M. Shanbhag; Artur M. Schweidtmann (CoRR, 2023)

Data augmentation for machine learning of chemical process flowsheets. - Lukas Schulze Balhorn; Edwin Hirtreiter; Lynn Luderer; Artur M. Schweidtmann (CoRR, 2023)

Deep reinforcement learning for process design: Review and perspective. - Qinghe Gao; Artur M. Schweidtmann (CoRR, 2023)

Data-driven Product-Process Optimization of N-isopropylacrylamide Microgel Flow-Synthesis. - Luise F. Kaven; Artur M. Schweidtmann; Jan Keil; Jana Israel; Nadja Wolter; Alexander Mitsos (CoRR, 2023)

What does ChatGPT know about natural science and engineering? - Lukas Schulze Balhorn; Jana M. Weber; Stefan Buijsman; Julian Romeo Hildebrandt; Martina Ziefle; Artur M. Schweidtmann (CoRR, 2023)

Toward autocorrection of chemical process flowsheets using large language models. - Lukas Schulze Balhorn; Marc Caballero; Artur M. Schweidtmann (CoRR, 2023)

Software for "Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction". - Artur M. Schweidtmann; Jan G. Rittig; Jana M. Weber; Martin Grohe; Manuel Dahmen; Kai Leonhard; Alexander Mitsos (Zenodo, 2023)

Geometry optimization of a continuous millireactor via CFD and Bayesian optimization. - Moritz J. Begall; Artur M. Schweidtmann; Adel Mhamdi; Alexander Mitsos (Comput. Chem. Eng., 2023)

Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids. - Jan G. Rittig; Karim Ben Hicham; Artur M. Schweidtmann; Manuel Dahmen; Alexander Mitsos (Comput. Chem. Eng., 2023)

Learning from flowsheets: A generative transformer model for autocompletion of flowsheets. - Gabriel Vogel; Lukas Schulze Balhorn; Artur M. Schweidtmann (Comput. Chem. Eng., 2023)

Physical pooling functions in graph neural networks for molecular property prediction. - Artur M. Schweidtmann; Jan G. Rittig; Jana M. Weber; Martin Grohe; Manuel Dahmen; Kai Leonhard; Alexander Mitsos (Comput. Chem. Eng., 2023)

Supplementary information to "What does ChatGPT know about natural science and engineering?". - Lukas Schulze Balhorn; Jana M. Weber; Stefan Buijsman; Julian Romeo Hildebrandt; Martina Ziefle; Artur M. Schweidtmann (Zenodo, 2023)

Mixed-integer optimisation of graph neural networks for computer-aided molecular design. - Tom McDonald; Calvin Tsay; Artur M. Schweidtmann; Neil Yorke-Smith (Comput. Chem. Eng., 2024)

MachineLearnAthon: An Action-Oriented Machine Learning Didactic Concept. - Michal Tkác; Jakub Sieber; Lara Kuhlmann; Matthias Brueggenolte; Alexandru Rinciog; Michael Henke 004; Artur M. Schweidtmann; Qinghe Gao; Maximilian F. Theisen; Radwa El Shawi (CoRR, 2024)

Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation. - Yidong Zhao; Joao Tourais; Iain Pierce; Christian Nitsche; Thomas A. Treibel; Sebastian Weingärtner; Artur M. Schweidtmann; Qian Tao (CoRR, 2024)

Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence. - Lukas Schulze Balhorn; Kevin Degens; Artur M. Schweidtmann (CoRR, 2024)

Machine learning in process systems engineering: Challenges and opportunities. - Prodromos Daoutidis; Jay H. Lee; Srinivas Rangarajan; Leo Chiang; R. Bhushan Gopaluni; Artur M. Schweidtmann; Iiro Harjunkoski; Mehmet Mercangöz; Ali Mesbah 002; Fani Boukouvala; Fernando V. Lima; Ehecatl Antonio del Rio-Chanona; et al. (Comput. Chem. Eng., 2024)

Accelerating process synthesis with reinforcement learning: Transfer learning from multi-fidelity simulations and variational autoencoders. - Qinghe Gao; Haoyu Yang; Maximilian F. Theisen; Artur M. Schweidtmann (Comput. Chem. Eng., 2025)

Picard-KKT-hPINN: Enforcing Nonlinear Enthalpy Balances for Physically Consistent Neural Networks. - Giacomo Lastrucci; Tanuj Karia; Zoë Gromotka; Artur M. Schweidtmann (CoRR, 2025)

ENFORCE: Exact Nonlinear Constrained Learning with Adaptive-depth Neural Projection. - Giacomo Lastrucci; Artur M. Schweidtmann (CoRR, 2025)

Transferring Graph Neural Networks for Soft Sensor Modeling using Process Topologies. - Maximilian F. Theisen; Gabrie M. H. Meesters; Artur M. Schweidtmann (CoRR, 2025)

Rule-based autocorrection of Piping and Instrumentation Diagrams (P&IDs) on graphs. - Lukas Schulze Balhorn; Niels Seijsener; Kevin Dao; Minji Kim; Dominik P. Goldstein; Ge H. M. Driessen; Artur M. Schweidtmann (CoRR, 2025)

Talking like Piping and Instrumentation Diagrams (P&IDs). - Achmad Anggawirya Alimin; Dominik P. Goldstein; Lukas Schulze Balhorn; Artur M. Schweidtmann (CoRR, 2025)

Text2Model: Generating dynamic chemical reactor models using large language models (LLMs). - Sophia Rupprecht; Yassine Hounat; Monisha Kumar; Giacomo Lastrucci; Artur M. Schweidtmann (CoRR, 2025)

Multi-agent systems for chemical engineering: A review and perspective. - Sophia Rupprecht; Qinghe Gao; Tanuj Karia; Artur M. Schweidtmann (CoRR, 2025)