3 papers
cs.RO2024
DisGNet: A Distance Graph Neural Network for Forward Kinematics Learning of Gough-Stewart Platform
Huizhi Zhu, Wenxia Xu, Jian Huang +1
In this paper, we propose a graph neural network, DisGNet, for learning the graph distance matrix to address the forward kinematics problem of the Gough-Stewart platform. DisGNet e…
cs.LG2024
BENO: Boundary-embedded Neural Operators for Elliptic PDEs
Haixin Wang, Jiaxin Li, Anubhav Dwivedi +2
Elliptic partial differential equations (PDEs) are a major class of time-independent PDEs that play a key role in many scientific and engineering domains such as fluid dynamics, pl…
astro-ph.IM2023
AI Techniques for Uncovering Resolved Planetary Nebula Candidates from Wide-field VPHAS+ Survey Data
Ruiqi Sun, Yushan Li, Quentin Parker +4
AI and deep learning techniques are beginning to play an increasing role in astronomy as a necessary tool to deal with the data avalanche. Here we describe an application for findi…