2 citations · 2 across the 3 of their papers we have counts for
3 papers
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…