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Found 16 papers, 0 papers with code
Global Optimization of Gaussian processes
Often, Gaussian processes are trained on datasets and are subsequently embedded as surrogate models in optimization problems.
Graph Machine Learning for Design of High-Octane Fuels
We propose a modular graph-ML CAMD framework that integrates generative graph-ML models with graph neural networks and optimization, enabling the design of molecules with desired ignition properties in a continuous molecular space.
Graph Neural Networks for Temperature-Dependent Activity Coefficient Prediction of Solutes in Ionic Liquids
We train the GNN on a database including more than 40, 000 AC values and compare it to a state-of-the-art MCM.
Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks
In particular, the graph neural networks are implemented within the agent architecture to process the states and make decisions.
Physical Pooling Functions in Graph Neural Networks for Molecular Property Prediction
We recommend using sum pooling for the prediction of properties that depend on molecular size and compare pooling functions for properties that are molecular size-independent.
Learning from flowsheets: A generative transformer model for autocompletion of flowsheets
We pre-train our model on synthetically generated flowsheets to learn the flowsheet language grammar.
SFILES 2.0: An extended text-based flowsheet representation
SFILES is a text-based notation for chemical process flowsheets.
Graph neural networks for the prediction of molecular structure-property relationships
Molecular property prediction is of crucial importance in many disciplines such as drug discovery, molecular biology, or material and process design.
Towards automatic generation of Piping and Instrumentation Diagrams (P&IDs) with Artificial Intelligence
Thereafter, the model is fine-tuned leveraging transfer learning on real P&IDs.
Data augmentation for machine learning of chemical process flowsheets
Artificial intelligence has great potential for accelerating the design and engineering of chemical processes.
Transfer learning for process design with reinforcement learning
We thus propose to utilize transfer learning for process design with RL in combination with rigorous simulation methods.
Deep reinforcement learning for process design: Review and perspective
The transformation towards renewable energy and feedstock supply in the chemical industry requires new conceptual process design approaches.
Mixed-Integer Optimisation of Graph Neural Networks for Computer-Aided Molecular Design
ReLU neural networks have been modelled as constraints in mixed integer linear programming (MILP), enabling surrogate-based optimisation in various domains and efficient solution of machine learning certification problems.
Toward autocorrection of chemical process flowsheets using large language models
The model achieves a top-1 accuracy of 80% and a top-5 accuracy of 84% on an independent test dataset of synthetically generated flowsheets.
MachineLearnAthon: An Action-Oriented Machine Learning Didactic Concept
Machine Learning (ML) techniques are encountered nowadays across disciplines, from social sciences, through natural sciences to engineering.
Bayesian Uncertainty Estimation by Hamiltonian Monte Carlo: Applications to Cardiac MRI Segmentation
Deep learning (DL)-based methods have achieved state-of-the-art performance for a wide range of medical image segmentation tasks.
