Deep Learning techniques have been increasingly used in flood management to overcome
the limitations of accurate, yet slow, numerical models, and to improve the results of …

Intelligent Mesh Generation (IMG) represents a novel and promising field of research,
utilizing machine learning techniques to generate meshes. Despite its relative infancy, IMG …

We introduce a new general-purpose approach to deep learning on three-dimensional
surfaces based on the insight that a simple diffusion layer is highly effective for spatial …

The present work proposes a framework for nonlinear model order reduction based on a
Graph Convolutional Autoencoder (GCA-ROM). In the reduced order modeling (ROM) …

Visual Simultaneous Localization and Map** (VSLAM) has attracted considerable
attention in recent years. This task involves using visual sensors to localize a robot while …

Generative models aim to learn the distribution of observed data by generating new
instances. With the advent of neural networks, deep generative models, including variational …

Reduced-order modelling and low-dimensional surrogate models generated using machine
learning algorithms have been widely applied in high-dimensional dynamical systems to …

We present a learning-based method for building driving-signal aware full-body avatars. Our
model is a conditional variational autoencoder that can be animated with incomplete driving …

This work investigates the use of robust optimal transport (OT) for shape matching.
Specifically, we show that recent OT solvers improve both optimization-based and deep …

High-dimensional dynamical systems often require computationally intensive physics-based
simulations, making full physical space data assimilation impractical. Latent data …