Variational autoencoder architectures have the potential to develop reduced-order models
for chaotic fluid flows. We propose a method for learning compact and near-orthogonal …

Variational autoencoders (VAEs) have shown promising potential as artificial neural
networks (NN) for developing reduced-order models (ROMs) in the context of turbulent …

This paper presents a mathematical analysis of semantic convergence in transformer-based
language models, drawing inspiration from the concept of fractal self-similarity. We introduce …

Flow control is key to maximize energy efficiency in a wide range of applications. However,
traditional flow-control methods face significant challenges in addressing non-linear systems …

Modeling of physical processes using dynamic evolution equations started in earnest with
Isaac Newton. Ordinary differential equations (ODE's) came first and Newton wrote to …

A deep-learning-based closure model to address energy loss in low-dimensional surrogate
models based on proper-orthogonal-decomposition (POD) modes is introduced. Using a …

Time series forecasting is a critical task in domains such as energy, finance, and
meteorology, where accurate long-term predictions are essential. While Transformer-based …

Koopman operator based models emerged as the leading methodology for machine
learning of dynamical systems. But their scope is much larger. In fact they present a new …

Las tecnologías de simulación se han convertido en una herramienta útil para modelizar
muchos sistemas en una amplia variedad de disciplinas, desde las ciencias sociales a las …