25 citations · 27 across the 5 of their papers we have counts for
5 papers
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…
A GPU-Accelerated Bi-linear ADMM Algorithm for Distributed Sparse Machine Learning
Alireza Olama, Andreas Lundell, Jan Kronqvist +2
This paper introduces the Bi-linear consensus Alternating Direction Method of Multipliers (Bi-cADMM), aimed at solving large-scale regularized Sparse Machine Learning (SML) problem…
Physics-Informed Neural Networks with Skip Connections for Modeling and Control of Gas-Lifted Oil Wells
Jonas Ekeland Kittelsen, Eric Aislan Antonelo, Eduardo Camponogara +1
Neural networks, while powerful, often lack interpretability. Physics-Informed Neural Networks (PINNs) address this limitation by incorporating physics laws into the loss function,…
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…
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…