196 citations · 700 across the 23 of their papers we have counts for
24 papers
Text2Model: Generating dynamic chemical reactor models using large language models (LLMs)
Sophia Rupprecht, Yassine Hounat, Monisha Kumar +2
As large language models have shown remarkable capabilities in conversing via natural language, the question arises as to how LLMs could potentially assist chemical engineers in re…
Deterministic Global Optimization over trained Kolmogorov Arnold Networks
Tanuj Karia, Giacomo Lastrucci, Artur M. Schweidtmann
To address the challenge of tractability for optimizing mathematical models in science and engineering, surrogate models are often employed. Recently, a new class of machine learni…
Transferring Graph Neural Networks for Soft Sensor Modeling using Process Topologies
Maximilian F. Theisen, Gabrie M. H. Meesters, Artur M. Schweidtmann
Data-driven soft sensors help in process operations by providing real-time estimates of otherwise hard- to-measure process quantities, e.g., viscosities or product concentrations.…
ENFORCE: Nonlinear Constrained Learning with Adaptive-depth Neural Projection
Giacomo Lastrucci, Artur M. Schweidtmann
Ensuring neural networks adhere to domain-specific constraints is crucial for addressing safety and trustworthiness while also enhancing inference accuracy. Despite the nonlinear n…
Picard-KKT-hPINN: Enforcing Nonlinear Enthalpy Balances for Physically Consistent Neural Networks
Giacomo Lastrucci, Tanuj Karia, Zoë Gromotka +1
Neural networks are widely used as surrogate models but they do not guarantee physically consistent predictions thereby preventing adoption in various applications. We propose a me…
Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation
Yidong Zhao, Joao Tourais, Iain Pierce +5
Deep learning (DL)-based methods have achieved state-of-the-art performance for many medical image segmentation tasks. Nevertheless, recent studies show that deep neural networks (…
MachineLearnAthon: An Action-Oriented Machine Learning Didactic Concept
Michal Tkáč, Jakub Sieber, Lara Kuhlmann +7
Machine Learning (ML) techniques are encountered nowadays across disciplines, from social sciences, through natural sciences to engineering. The broad application of ML and the acc…
Mixed-Integer Optimisation of Graph Neural Networks for Computer-Aided Molecular Design
Tom McDonald, Calvin Tsay, Artur M. Schweidtmann +1
ReLU neural networks have been modelled as constraints in mixed integer linear programming (MILP), enabling surrogate-based optimisation in various domains and efficient solution o…
Data-driven Product-Process Optimization of N-isopropylacrylamide Microgel Flow-Synthesis
Luise F. Kaven, Artur M. Schweidtmann, Jan Keil +3
Microgels are cross-linked, colloidal polymer networks with great potential for stimuli-response release in drug-delivery applications, as their size in the nanometer range allows…
Deep reinforcement learning for process design: Review and perspective
Qinghe Gao, Artur M. Schweidtmann
The transformation towards renewable energy and feedstock supply in the chemical industry requires new conceptual process design approaches. Recently, breakthroughs in artificial i…
Data augmentation for machine learning of chemical process flowsheets
Lukas Schulze Balhorn, Edwin Hirtreiter, Lynn Luderer +1
Artificial intelligence has great potential for accelerating the design and engineering of chemical processes. Recently, we have shown that transformer-based language models can le…
Transfer learning for process design with reinforcement learning
Qinghe Gao, Haoyu Yang, Shachi M. Shanbhag +1
Process design is a creative task that is currently performed manually by engineers. Artificial intelligence provides new potential to facilitate process design. Specifically, rein…
Efficient Bayesian Uncertainty Estimation for nnU-Net
Yidong Zhao, Changchun Yang, Artur Schweidtmann +1
The self-configuring nnU-Net has achieved leading performance in a large range of medical image segmentation challenges. It is widely considered as the model of choice and a strong…
SFILES 2.0: An extended text-based flowsheet representation
Gabriel Vogel, Edwin Hirtreiter, Lukas Schulze Balhorn +1
SFILES are a text-based notation for chemical process flowsheets. They were originally proposed by d'Anterroches (Process flow sheet generation & design through a group contributio…
Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction
Artur M. Schweidtmann, Jan G. Rittig, Jana M. Weber +4
Graph neural networks (GNNs) are emerging in chemical engineering for the end-to-end learning of physicochemical properties based on molecular graphs. A key element of GNNs is the…
Optimization with Trained Machine Learning Models Embedded
Artur M. Schweidtmann, Dominik Bongartz, Alexander Mitsos
Trained ML models are commonly embedded in optimization problems. In many cases, this leads to large-scale NLPs that are difficult to solve to global optimality. While ML models fr…
Graph neural networks for the prediction of molecular structure-property relationships
Jan G. Rittig, Qinghe Gao, Manuel Dahmen +2
Molecular property prediction is of crucial importance in many disciplines such as drug discovery, molecular biology, or material and process design. The frequently employed quanti…
Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks
Laura Stops, Roel Leenhouts, Qinghe Gao +1
Process synthesis experiences a disruptive transformation accelerated by digitization and artificial intelligence. We propose a reinforcement learning algorithm for chemical proces…
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids
Jan G. Rittig, Karim Ben Hicham, Artur M. Schweidtmann +2
Ionic liquids (ILs) are important solvents for sustainable processes and predicting activity coefficients (ACs) of solutes in ILs is needed. Recently, matrix completion methods (MC…
Graph Machine Learning for Design of High-Octane Fuels
Jan G. Rittig, Martin Ritzert, Artur M. Schweidtmann +7
Fuels with high-knock resistance enable modern spark-ignition engines to achieve high efficiency and thus low CO2 emissions. Identification of molecules with desired autoignition p…
Multi-scale membrane process optimization with high-fidelity ion transport models through machine learning
Deniz Rall, Artur M. Schweidtmann, Maximilian Krusea +3
Innovative membrane technologies optimally integrated into large separation process plants are essential for economical water treatment and disposal. However, the mass transport th…
Obey validity limits of data-driven models
Artur M Schweidtmann, Jana M Weber, Christian Wende +2
Data-driven models are becoming increasingly popular in engineering, on their own or in combination with mechanistic models. Commonly, the trained models are subsequently used in m…
Global Optimization of Gaussian processes
Artur M. Schweidtmann, Dominik Bongartz, Daniel Grothe +4
Gaussian processes~(Kriging) are interpolating data-driven models that are frequently applied in various disciplines. Often, Gaussian processes are trained on datasets and are subs…
Global Deterministic Optimization with Artificial Neural Networks Embedded
Artur M Schweidtmann, Alexander Mitsos
Artificial neural networks (ANNs) are used in various applications for data-driven black-box modeling and subsequent optimization. Herein, we present an efficient method for determ…