AS
Artur M. Schweidtmann
cs.LGmath.OCcs.AIcs.CEcs.CLcs.CVstat.MLbiomassbiotechnologycircular economy
On Valency
published · living versionsW_5yf98b9q·v1 · currentpublished
Preprints & journals
42 papers in the corpus · 2017–2026Pruning for efficient deterministic global optimization over trained ReLU neural networks2603.23299v2 · Giacomo Lastrucci, Tanuj Karia, Victor Schulte et al.2026 · 0 citationsarXiv
GraphRAG for Engineering Diagrams: ChatP&ID Enables LLM Interaction with P&IDs2603.22528v1 · Achmad Anggawirya Alimin, Artur M. Schweidtmann2026 · 0 citationsarXiv
Grounding Large Language Models in Reaction Knowledge Graphs for Synthesis Retrieval2601.16038v1 · Olga Bunkova, Lorenzo Di Fruscia, Sophia Rupprecht et al.2026 · 0 citationsarXiv
Adaptive experimentation and optimization in organic chemistry.41220841 · Schweidtmann, Artur M, Schwaller, Philippe2025 · 0 citationsBeilstein journal of organic chemistry. 2025;21:2367-2368
Multi-agent systems for chemical engineering: A review and perspective2508.07880v1 · Sophia Rupprecht, Qinghe Gao, Tanuj Karia et al.2025 · 19 citationsarXiv
Text2Model: Generating dynamic chemical reactor models using large language models (LLMs)2503.17004v1 · Sophia Rupprecht, Yassine Hounat, Monisha Kumar et al.2025 · 7 citationsarXiv
Deterministic Global Optimization over trained Kolmogorov Arnold Networks2503.02807v1 · Tanuj Karia, Giacomo Lastrucci, Artur M. Schweidtmann2025 · 0 citationsarXiv
Talking like Piping and Instrumentation Diagrams (P&IDs)2502.18928v1 · Achmad Anggawirya Alimin, Dominik P. Goldstein, Lukas Schulze Balhorn et al.2025 · 8 citationsarXiv
Rule-based autocorrection of Piping and Instrumentation Diagrams (P&IDs) on graphs2502.18493v2 · Lukas Schulze Balhorn, Niels Seijsener, Kevin Dao et al.2025 · 8 citationsSystems and Control Transactions, Volume 4, 2025, Pages 1656-1661
ENFORCE: Nonlinear Constrained Learning with Adaptive-depth Neural Projection2502.06774v4 · Giacomo Lastrucci, Artur M. Schweidtmann2025 · 4 citationsarXiv
Transferring Graph Neural Networks for Soft Sensor Modeling using Process Topologies2502.06826v1 · Maximilian F. Theisen, Gabrie M. H. Meesters, Artur M. Schweidtmann2025 · 5 citationsarXiv
Picard-KKT-hPINN: Enforcing Nonlinear Enthalpy Balances for Physically Consistent Neural Networks2501.17782v1 · Giacomo Lastrucci, Tanuj Karia, Zoe Gromotka et al.2025 · 7 citationsarXiv
Graph-to-SFILES: Control structure prediction from process topologies using generative artificial intelligence2412.00508v2 · Lukas Schulze Balhorn, Kevin Degens, Artur M. Schweidtmann2024 · 10 citationsComputers & Chemical Engineering, Volume 199, 2025, Pages 109121
Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation2403.02311v3 · Yidong Zhao, Joao Tourais, Iain Pierce et al.2024 · 5 citationsMachine.Learning.for.Biomedical.Imaging. 2 (2024)
Empirical assessment of ChatGPT's answering capabilities in natural science and engineering.38424125 · Schulze Balhorn, Lukas, Weber, Jana M, Buijsman, Stefan et al.2024 · 42 citationsScientific reports. 2024;14(1):4998
MachineLearnAthon: An Action-Oriented Machine Learning Didactic Concept2401.16291v1 · Michal Tk'ac, Jakub Sieber, Lara Kuhlmann et al.2024 · 0 citationsarXiv
Toward autocorrection of chemical process flowsheets using large language models2312.02873v2 · Lukas Schulze Balhorn, Marc Caballero, Artur M. Schweidtmann2023 · 17 citationsComputer Aided Chemical Engineering, Volume 53, 2024, Pages 3109-3114
Mixed-Integer Optimisation of Graph Neural Networks for Computer-Aided Molecular Design2312.01228v1 · Tom McDonald, Calvin Tsay, Artur M. Schweidtmann et al.2023 · 17 citationsarXiv
Data-driven Product-Process Optimization of N-isopropylacrylamide Microgel Flow-Synthesis2308.16724v1 · Luise F. Kaven, Artur M. Schweidtmann, Jan Keil et al.2023 · 11 citationsarXiv
Deep reinforcement learning for process design: Review and perspective2308.07822v2 · Qinghe Gao, Artur M. Schweidtmann2023 · 64 citationsarXiv
Transfer learning for process design with reinforcement learning2302.03375v1 · Qinghe Gao, Haoyu Yang, Shachi M. Shanbhag et al.2023 · 13 citationsarXiv
Data augmentation for machine learning of chemical process flowsheets2302.03379v1 · Lukas Schulze Balhorn, Edwin Hirtreiter, Lynn Luderer et al.2023 · 5 citationsComputer Aided Chemical Engineering Volume 52, 2023, Pages 2011-2016
Efficient Bayesian Uncertainty Estimation for nnU-Net2212.06278v3 · Yidong Zhao, Changchun Yang, Artur Schweidtmann et al.2022 · 31 citationsarXiv
Toward automatic generation of control structures for process flow diagrams with large language models2211.05583v2 · Edwin Hirtreiter, Lukas Schulze Balhorn, Artur M. Schweidtmann2022 · 42 citationsAIChE Journal, Volume 70, Issue 1, January 2024, e18259
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets2208.00859v2 · Gabriel Vogel, Lukas Schulze Balhorn, Artur M. Schweidtmann2022 · 64 citationsComputers and Chemical Engineering Volume 171, March 2023, 108162
Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction2207.13779v1 · Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber et al.2022 · 52 citationsComputers and Chemical Engineering Volume 172, April 2023, 108202
Optimization with Trained Machine Learning Models Embedded2207.12722v1 · Artur M. Schweidtmann, Dominik Bongartz, Alexander Mitsos2022 · 6 citationsEncyclopedia of Optimization (pp. 1-8). Cham: Springer International Publishing, 2022
Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks2207.12051v1 · Laura Stops, Roel Leenhouts, Qinghe Gao et al.2022 · 56 citationsAIChE Journal, Volume 69, Issue1 January 2023 e17938
SFILES 2.0: An extended text-based flowsheet representation2208.00778v2 · Gabriel Vogel, Edwin Hirtreiter, Lukas Schulze Balhorn et al.2022 · 29 citationsOptimization and Engineering, Volume 24, pages 2911-2933, (2023)
Graph neural networks for the prediction of molecular structure-property relationships2208.04852v1 · Jan G. Rittig, Qinghe Gao, Manuel Dahmen et al.2022 · 25 citationsMachine Learning and Hybrid Modelling for Reaction Engineering, Royal Society of Chemistry, ISBN 978-1-83916-563-4, 159-181, 2023
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids2206.11776v1 · Jan G. Rittig, Karim Ben Hicham, Artur M. Schweidtmann et al.2022 · 82 citationsComputers & Chemical Engineering 171, 108153, 2023
Graph Machine Learning for Design of High-Octane Fuels2206.00619v2 · Jan G. Rittig, Martin Ritzert, Artur M. Schweidtmann et al.2022 · 36 citationsAIChE Journal 69 (4), e17971, 2023
Multi-scale membrane process optimization with high-fidelity ion transport models through machine learning2204.12741v1 · Deniz Rall, Artur M. Schweidtmann, Maximilian Krusea et al.2022 · 74 citationsJournal of Membrane Science, Volume 608, 1 August 2020, 118208
Chemical data intelligence for sustainable chemistry.34520507 · Weber, Jana M, Guo, Zhen, Zhang, Chonghuan et al.2021 · 70 citationsChemical Society reviews. 2021;50(21):12013-12036
Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb.36303772 · Liebal, Ulf W, Köbbing, Sebastian, Netze, Linus et al.2026 · 9 citationsFrontiers in bioinformatics. 2021;1:747428
Obey validity limits of data-driven models2010.03405v1 · Artur M Schweidtmann, Jana M Weber, Christian Wende et al.2020 · 57 citationsVolume 23, pages 855-876, (2022)
Pushing Nanomaterials past the Kilogram Scale—a Targeted Approach Integrating Scalable Microreactors, Machine Learning and High-Throughput Analysis10.26434/chemrxiv.12732914.v1 · Nicholas Jose, mikhail Kovalev, Eric Bradford et al.2020 · 0 citationsChemRxiv
Global Optimization of Gaussian processes2005.10902v1 · Artur M. Schweidtmann, Dominik Bongartz, Daniel Grothe et al.2020 · 60 citationsMath. Prog. Comp. 13, 553-581 (2021)
Graph Neural Networks for Prediction of Fuel Ignition Quality10.26434/chemrxiv.12280325.v1 · Artur Schweidtmann, Jan Rittig, Andrea König et al.2020 · 140 citationsChemRxiv
Machine learning and molecular descriptors enable rational solvent selection in asymmetric catalysis.31367324 · Amar, Yehia, Schweidtmann, Artur M, Deutsch, Paul et al.2023 · 132 citationsChemical science. 2019;10(27):6697-6706
Global Deterministic Optimization with Artificial Neural Networks Embedded1801.07114v2 · Artur M Schweidtmann, Alexander Mitsos2018 · 218 citationsarXiv
A Multiobjective Optimization Including Results of Life Cycle Assessment in Developing Biorenewables-Based Processes.28714562 · Helmdach, Daniel, Yaseneva, Polina, Heer, Parminder K et al.2017 · 46 citationsChemSusChem. 2017;10(18):3632-3643
Career total: 166 works. 42 are in this corpus.
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