collaborators

22 papers

cs.LG2025

Stochastic Fractional Neural Operators: A Symmetrized Approach to Modeling Turbulence in Complex Fluid Dynamics

Rômulo Damasclin Chaves dos Santos, Jorge Henrique de Oliveira Sales

In this work, we introduce a new class of neural network operators designed to handle problems where memory effects and randomness play a central role. In this work, we introduce a…

cs.LG2025

Revolutionizing Fractional Calculus with Neural Networks: Voronovskaya-Damasclin Theory for Next-Generation AI Systems

Rômulo Damasclin Chaves dos Santos, Jorge Henrique de Oliveira Sales

This work introduces rigorous convergence rates for neural network operators activated by symmetrized and perturbed hyperbolic tangent functions, utilizing novel Voronovskaya-Damas…

math.GM2025

Advancements in Fractional Neural Operators with Adaptive Hybrid Kernels in Multiscale Sobolev Spaces

Romulo Damaselin Chaves dos Santos, Jorge Henrique de Oliveira Sales

This paper introduces significant advancements in fractional neural operators (FNOs) through the integration of adaptive hybrid kernels and stochastic multiscale analysis. We addre…

math.GM2025

Generalized Neural Network Operators with Symmetrized Activations: Fractional Convergence and the Voronovskaya-Damasclin Theorem

Rômulo Damasclin Chaves dos Santos

This paper explores the asymptotic behavior of univariate neural network operators, with an emphasis on both classical and fractional differentiation over infinite domains. The ana…

stat.ML2025

Extension of Symmetrized Neural Network Operators with Fractional and Mixed Activation Functions

Rômulo Damasclin Chaves dos Santos, Jorge Henrique de Oliveira Sales

We propose a novel extension to symmetrized neural network operators by incorporating fractional and mixed activation functions. This study addresses the limitations of existing mo…

math.GM2025

Non-Linear Interactions in Neural Network Operators: New Theorems on Symmetry-Preserving Transformations

Rômulo Damasclin Chaves dos Santos, Jorge Henrique de Oliveira Sales

This paper advances the study of multivariate function approximation using neural network operators activated by symmetrized and perturbed hyperbolic tangent functions. We propose…