Artur M. Schweidtmann

Orcid: 0000-0001-8885-6847

According to our database1, Artur M. Schweidtmann authored at least 35 papers between 2018 and 2024.

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Bibliography

2024
Machine learning in process systems engineering: Challenges and opportunities.
Comput. Chem. Eng., February, 2024

Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation.
CoRR, 2024

MachineLearnAthon: An Action-Oriented Machine Learning Didactic Concept.
CoRR, 2024

Mixed-integer optimisation of graph neural networks for computer-aided molecular design.
Comput. Chem. Eng., 2024

2023
Supplementary information to "What does ChatGPT know about natural science and engineering?".
Dataset, September, 2023

Physical pooling functions in graph neural networks for molecular property prediction.
Comput. Chem. Eng., April, 2023

Learning from flowsheets: A generative transformer model for autocompletion of flowsheets.
Comput. Chem. Eng., March, 2023

Graph neural networks for temperature-dependent activity coefficient prediction of solutes in ionic liquids.
Comput. Chem. Eng., March, 2023

Geometry optimization of a continuous millireactor via CFD and Bayesian optimization.
Comput. Chem. Eng., March, 2023

Software for "Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction".
Dataset, January, 2023

Toward autocorrection of chemical process flowsheets using large language models.
CoRR, 2023

What does ChatGPT know about natural science and engineering?
CoRR, 2023

Data-driven Product-Process Optimization of N-isopropylacrylamide Microgel Flow-Synthesis.
CoRR, 2023

Deep reinforcement learning for process design: Review and perspective.
CoRR, 2023

Data augmentation for machine learning of chemical process flowsheets.
CoRR, 2023

Transfer learning for process design with reinforcement learning.
CoRR, 2023

2022
Towards automatic generation of Piping and Instrumentation Diagrams (P&IDs) with Artificial Intelligence.
CoRR, 2022

Graph neural networks for the prediction of molecular structure-property relationships.
CoRR, 2022

SFILES 2.0: An extended text-based flowsheet representation.
CoRR, 2022

Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks.
CoRR, 2022

Graph Machine Learning for Design of High-Octane Fuels.
CoRR, 2022

HybridML: Open source platform for hybrid modeling.
Comput. Chem. Eng., 2022

Efficient Bayesian Uncertainty Estimation for nnU-Net.
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2022, 2022

2021
Global optimization of processes through machine learning.
PhD thesis, 2021

Deterministic global optimization with Gaussian processes embedded.
Math. Program. Comput., 2021

Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb.
Frontiers Bioinform., 2021

2020
Global Optimization of Gaussian processes.
CoRR, 2020

Wavelet-based grid-adaptation for nonlinear scheduling subject to time-variable electricity prices.
Comput. Chem. Eng., 2020

Deterministic global superstructure-based optimization of an organic Rankine cycle.
Comput. Chem. Eng., 2020

2019
Deterministic Global Optimization with Artificial Neural Networks Embedded.
J. Optim. Theory Appl., 2019

Deterministic global process optimization: Accurate (single-species) properties via artificial neural networks.
Comput. Chem. Eng., 2019

Model-based bidding strategies on the primary balancing market for energy-intense processes.
Comput. Chem. Eng., 2019

2018
Correction to: Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm.
J. Glob. Optim., 2018

Efficient multiobjective optimization employing Gaussian processes, spectral sampling and a genetic algorithm.
J. Glob. Optim., 2018

Dynamic modeling and optimization of sustainable algal production with uncertainty using multivariate Gaussian processes.
Comput. Chem. Eng., 2018


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