219 citations · 323 across the 6 of their papers we have counts for
9 papers
Machine learning in wastewater treatment: insights from modelling a pilot denitrification reactor
Eivind Bøhn, Sølve Eidnes, Kjell Rune Jonassen
Wastewater treatment plants are increasingly recognized as promising candidates for machine learning applications, due to their societal importance and high availability of data. H…
Recency-Weighted Temporally-Segmented Ensemble for Time-Series Modeling
Pål V. Johnsen, Eivind Bøhn, Sølve Eidnes +2
Time-series modeling in process industries faces the challenge of dealing with complex, multi-faceted, and evolving data characteristics. Conventional single model approaches often…
Pseudo-Hamiltonian Neural Networks with State-Dependent External Forces
Sølve Eidnes, Alexander J. Stasik, Camilla Sterud +2
Hybrid machine learning based on Hamiltonian formulations has recently been successfully demonstrated for simple mechanical systems, both energy conserving and not energy conservin…
Optimization of the Model Predictive Control Meta-Parameters Through Reinforcement Learning
Eivind Bøhn, Sebastien Gros, Signe Moe +1
Model predictive control (MPC) is increasingly being considered for control of fast systems and embedded applications. However, the MPC has some significant challenges for such sys…
Data-Efficient Deep Reinforcement Learning for Attitude Control of Fixed-Wing UAVs: Field Experiments
Eivind Bøhn, Erlend M. Coates, Dirk Reinhardt +1
Attitude control of fixed-wing unmanned aerial vehicles (UAVs) is a difficult control problem in part due to uncertain nonlinear dynamics, actuator constraints, and coupled longitu…
Reinforcement Learning of the Prediction Horizon in Model Predictive Control
Eivind Bøhn, Sebastien Gros, Signe Moe +1
Model predictive control (MPC) is a powerful trajectory optimization control technique capable of controlling complex nonlinear systems while respecting system constraints and ensu…