3 citations · 8 across the 13 of their papers we have counts for
4 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…
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…
Deterministic Global Optimization of the Acquisition Function in Bayesian Optimization: To Do or Not To Do?
Anastasia Georgiou, Daniel Jungen, Luise Kaven +4
Bayesian Optimization (BO) with Gaussian Processes relies on optimizing an acquisition function to determine sampling. We investigate the advantages and disadvantages of using a de…