25 citations · 29 across the 3 of their papers we have counts for
3 papers
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,…
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…
Face Reconstruction with Variational Autoencoder and Face Masks
Rafael S. Toledo, Eric A. Antonelo
Variational AutoEncoders (VAE) employ deep learning models to learn a continuous latent z-space that is subjacent to a high-dimensional observed dataset. With that, many tasks are…