26 citations · 72 across the 18 of their papers we have counts for
4 papers · 1 filter
Neuro-Spectral Architectures for Causal Physics-Informed Networks
Arthur Bizzi, Leonardo M. Moreira, Márcio Marques +9
Physics-Informed Neural Networks (PINNs) have emerged as a powerful framework for solving partial differential equations (PDEs). However, standard MLP-based PINNs often fail to con…
Tuning the Frequencies: Robust Training for Sinusoidal Neural Networks
Tiago Novello, Diana Aldana, Andre Araujo +1
Sinusoidal neural networks have been shown effective as implicit neural representations (INRs) of low-dimensional signals, due to their smoothness and high representation capacity.…
Understanding Sinusoidal Neural Networks
Tiago Novello
In this work, we investigate the structure and representation capacity of sinusoidal MLPs - multilayer perceptron networks that use sine as the activation function. These neural ne…
Neural Implicit Surface Evolution
Tiago Novello, Vinicius da Silva, Guilherme Schardong +3
This work investigates the use of smooth neural networks for modeling dynamic variations of implicit surfaces under the level set equation (LSE). For this, it extends the represent…