Classifying land cover using high-resolution remote-sensing images is challenging. The
emergence of deep learning provides improved possibilities, but owing to the limitations of …

Developing robust constitutive models is a fundamental and longstanding problem for
accelerating the simulation of multiscale physics. Machine learning provides promising tools …

Graph neural networks (GNNs) have shown promise in learning unstructured mesh-based
simulations of physical systems, including fluid dynamics. In tandem, geometric deep …

This paper investigates the super-resolution of velocity fields in two-dimensional flows from
the viewpoint of rotational equivariance. Super-resolution refers to techniques that enhance …

Diffusion models have become the leading distribution-learning method in recent years.
Herein, we introduce structure-preserving diffusion processes, a family of diffusion …

Content-Based Image Retrieval (CBIR) is the main stay of current image retrieval systems
where a user submits an image based query which is then used by the system to extract …

The rapid growth of fields such as metamaterials, composites, architected materials, porous
solids, and micro/nano materials, along with the continuing advancements in design and …

In recent years, diffusion models have risen to prominence as the foremost technique for
distribution learning. This paper focuses on structure-preserving diffusion models (SPDM), a …

Recent work has constructed neural networks that are equivariant to continuous symmetry
groups such as 2D and 3D rotations. This is accomplished using explicit {\it Lie group …

Graph neural networks (GNNs) have shown promise in learning unstructured mesh-based
simulations of physical systems, including fluid dynamics. In tandem, geometric deep …