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Found 6 papers, 1 papers with code
Physics-Informed Neural Networks with Skip Connections for Modeling and Control of Gas-Lifted Oil Wells
Neural networks, while powerful, often lack interpretability.
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
Vertex-based reachability analysis for verifying ReLU deep neural networks
Neural networks achieved high performance over different tasks, i. e. image identification, voice recognition and other applications.
Investigation of Proper Orthogonal Decomposition for Echo State Networks
To this end, this work aims to investigate and analyze the performance of POD methods in Echo State Networks, evaluating their effectiveness through the Memory Capacity (MC) of the POD-reduced network compared to the original (full-order) ESN.
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.
