84 citations · 277 across the 26 of their papers we have counts for
5 papers · 1 filter
End-to-End Reinforcement Learning of Koopman Models for eNMPC of an Air Separation Unit
Daniel Mayfrank, Kayra Dernek, Laura Lang +2
With our recently proposed method based on reinforcement learning (Mayfrank et al. (2024), Comput. Chem. Eng. 190), Koopman surrogate models can be trained for optimal performance…
DeepEOSNet: Capturing the dependency on thermodynamic state in property prediction tasks
Jan Pavšek, Alexander Mitsos, Manuel Dahmen +2
We propose a machine learning (ML) architecture to better capture the dependency of thermodynamic properties on the independent states. When predicting state-dependent thermodynami…
Molecular Machine Learning in Chemical Process Design
Jan G. Rittig, Manuel Dahmen, Martin Grohe +2
We present a perspective on molecular machine learning (ML) in the field of chemical process engineering. Recently, molecular ML has demonstrated great potential in (i) providing h…
Accelerating Deterministic Global Optimization via GPU-parallel Interval Arithmetic
Hongzhen Zhang, Tim Kerkenhoff, Neil Kichler +4
Spatial Branch and Bound (B&B) algorithms are widely used for solving nonconvex problems to global optimality, yet they remain computationally expensive. Though some works have bee…
Sample-Efficient Reinforcement Learning of Koopman eNMPC
Daniel Mayfrank, Mehmet Velioglu, Alexander Mitsos +1
Reinforcement learning (RL) can be used to tune data-driven (economic) nonlinear model predictive controllers ((e)NMPCs) for optimal performance in a specific control task by optim…