Artur Schweidtmann

Latest

• Physics-informed neural networks and time-series transformer for modeling of chemical reactors
• Toward autocorrection of chemical process flowsheets using large language models
• Data augmentation for machine learning of chemical process flowsheets
• Transfer learning for process design with reinforcement learning
• Efficient Bayesian Uncertainty Estimation for nnU-Net
• Flowsheet Recognition using Deep Convolutional Neural Networks
• Hybrid mechanistic data-driven modeling for the deterministic global optimization of a transcritical organic Rankine cycle
• Modelling circular structures in reaction networks: Petri nets and reaction network flux analysis
• Deterministic global nonlinear model predictive control with neural networks embedded
• Impact of accurate working fluid properties on the globally optimal design of an organic Rankine cycle
• Deterministic global process optimization: Flash calculations via artificial neural networks