142 citations · 146 across the 8 of their papers we have counts for
6 papers · 1 filter
Solving Differential Equations using Physics-Informed Deep Equilibrium Models
Bruno Machado Pacheco, Eduardo Camponogara
This paper introduces Physics-Informed Deep Equilibrium Models (PIDEQs) for solving initial value problems (IVPs) of ordinary differential equations (ODEs). Leveraging recent advan…
Deep-learning-based Early Fixing for Gas-lifted Oil Production Optimization: Supervised and Weakly-supervised Approaches
Bruno Machado Pacheco, Laio Oriel Seman, Eduardo Camponogara
Maximizing oil production from gas-lifted oil wells entails solving Mixed-Integer Linear Programs (MILPs). As the parameters of the wells, such as the basic-sediment-to-water ratio…
Graph Neural Networks for the Offline Nanosatellite Task Scheduling Problem
Bruno Machado Pacheco, Laio Oriel Seman, Cezar Antonio Rigo +3
This study investigates how to schedule nanosatellite tasks more efficiently using Graph Neural Networks (GNNs). In the Offline Nanosatellite Task Scheduling (ONTS) problem, the go…
Vertex-based reachability analysis for verifying ReLU deep neural networks
João Zago, Eduardo Camponogara, Eric Antonelo
Neural networks achieved high performance over different tasks, i.e. image identification, voice recognition and other applications. Despite their success, these models are still v…
Investigation of Proper Orthogonal Decomposition for Echo State Networks
Jean Panaioti Jordanou, Eric Aislan Antonelo, Eduardo Camponogara +1
Echo State Networks (ESN) are a type of Recurrent Neural Network that yields promising results in representing time series and nonlinear dynamic systems. Although they are equipped…
Physics-Informed Neural Nets for Control of Dynamical Systems
Eric Aislan Antonelo, Eduardo Camponogara, Laio Oriel Seman +3
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…