We introduce generative models for accelerating simulations of high-dimensional systems
through learning and evolving their effective dynamics. In the proposed Generative Learning …

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

Particle accelerators are complex systems that focus, guide, and accelerate intense charged
particle beams to high energy. Beam diagnostics present a challenging problem due to …

Developing fast surrogates for Partial Differential Equations (PDEs) will accelerate design
and optimization in almost all scientific and engineering applications. Neural networks have …

In this work, we present GAROM, a new approach for reduced order modeling (ROM) based
on generative adversarial networks (GANs). GANs attempt to learn to generate data with the …

Abstract We introduce Branched Latent Neural Maps (BLNMs) to learn finite dimensional
input–output maps encoding complex physical processes. A BLNM is defined by a simple …

To improve the robustness of transformer neural networks used for temporal-dynamics
prediction of chaotic systems, we propose a novel attention mechanism called easy …

The aim of this work is to analyse the formation mechanisms of large-scale coherent
structures in the flow around a wall-mounted square cylinder, due to their impact on pollutant …

A machine learning framework is developed to compute the aerodynamic forces and
moment coefficients for a pitching NACA0012 airfoil incurring in light and deep dynamic …

Technological advancements have substantially increased computational power and data
availability, enabling the application of powerful machine-learning (ML) techniques across …