EurekAlert!

A physics-constrained AI framework enables accurate thermal field inversion for chiplet-based packaging with sparse data

Researchers at Shanghai University have developed a physics-constrained, data-efficient artificial intelligence framework ...
IEEE

Gaussian Process Kernels for Efficient Sequential Sampling of Electromagnetic Radiation on Spherical Surfaces

Abstract: Traditional far-field characterization of wireless devices relies on dense, fixed-grid measurements over the entire measurement surface, leading to high time and cost overheads. This paper ...
IEEE

Distributed Thompson Sampling Under Constrained Communication

Abstract: In Bayesian optimization, a black-box function is maximized via the use of a surrogate model. We apply distributed Thompson sampling, using a Gaussian process as a surrogate model, to ...
GitHub

2D Gaussian Splatting for Geometrically Accurate Radiance Fields

Project page | Paper | Video | Surfel Rasterizer (CUDA) | Surfel Rasterizer (Python) | DTU+COLMAP (3.5GB) | SIBR Viewer Pre-built for Windows | Web Viewer This repo contains the official ...
GitHub

Is There a Better Source Distribution than Gaussian?: Exploring Source Distributions for Image Flow Matching

Official implementation of our TMLR submission investigating optimal source distribution choices for flow matching in generative modeling. We introduce a novel 2D simulation framework that captures ...