Search Results for author:
Found 3 papers, 1 papers with code
Deep-learning-based Early Fixing for Gas-lifted Oil Production Optimization: Supervised and Weakly-supervised Approaches
Maximizing oil production from gas-lifted oil wells entails solving Mixed-Integer Linear Programs (MILPs).
Graph Neural Networks for the Offline Nanosatellite Task Scheduling Problem
This study examines the use of GNNs in this context, which has been effectively applied to optimization problems such as the traveling salesman, scheduling, and facility placement problems.
Combinatorial Optimization
Explainable Artificial Intelligence (XAI)
+1
Physics-Informed Neural Nets for Control of Dynamical Systems
Physics-informed neural networks (PINNs) impose known physical laws into the learning of deep neural networks, making sure they respect the physics of the process while decreasing the demand of labeled data.
