We propose a method that leverages graph neural networks, multi-level message passing,
and unsupervised training to enable real-time prediction of realistic clothing dynamics …

This paper explores the recent advancements in enhancing Computational Fluid Dynamics
(CFD) tasks through Machine Learning (ML) techniques. We begin by introducing …

Mechanics-related tasks often present unique challenges in achieving accurate geometric
and physical representations, particularly for non-uniform structures. Graph neural networks …

The goal of this work is to address two limitations in autoencoder-based models: latent
space interpretability and compatibility with unstructured meshes. This is accomplished here …

Recently, many mesh-based graph neural network (GNN) models have been proposed for
modeling complex high-dimensional physical systems. Remarkable achievements have …

High-accuracy, high-efficiency physics-based fluid-solid interaction is essential for reality
modeling and computer animation in online games or real-time Virtual Reality (VR) systems …

Modeling object dynamics with a neural network is an important problem with numerous
applications. Most recent work has been based on graph neural networks. However physics …

Abstract Mesh-based Graph Neural Networks (GNNs) have recently shown capabilities to
simulate complex multiphysics problems with accelerated performance times. However …

An emerging trend in deep learning research focuses on the applications of graph neural
networks (GNNs) for mesh-based continuum mechanics simulations. Most of these learning …

Operation stability significantly impacts hydrocyclone separation performance during
wastewater treatment, sludge processing, and microplastic removal from water. The air core …