10 citations · 12 across the 4 of their papers we have counts for
4 papers
Optimizing Energy Management of Smart Grid using Reinforcement Learning aided by Surrogate models built using Physics-informed Neural Networks
Julen Cestero, Carmine Delle Femine, Kenji S. Muro +2
Optimizing the energy management within a smart grids scenario presents significant challenges, primarily due to the complexity of real-world systems and the intricate interactions…
Building surrogate models using trajectories of agents trained by Reinforcement Learning
Julen Cestero, Marco Quartulli, Marcello Restelli
Sample efficiency in the face of computationally expensive simulations is a common concern in surrogate modeling. Current strategies to minimize the number of samples needed are no…
Limitations of Physics-Informed Neural Networks: a Study on Smart Grid Surrogation
Julen Cestero, Carmine Delle Femine, Kenji S. Muro +2
Physics-Informed Neural Networks (PINNs) present a transformative approach for smart grid modeling by integrating physical laws directly into learning frameworks, addressing critic…
Storehouse: a Reinforcement Learning Environment for Optimizing Warehouse Management
Julen Cestero, Marco Quartulli, Alberto Maria Metelli +1
Warehouse Management Systems have been evolving and improving thanks to new Data Intelligence techniques. However, many current optimizations have been applied to specific cases or…