SE3 Equivariant Hemodynamic Field Estimation in 3D Artery Models via Graph Neural Networks
Offered By: Conference GSI via YouTube
Course Description
Overview
Explore a cutting-edge approach to hemodynamic field estimation in 3D artery models through a 20-minute conference talk at GSI. Delve into the application of SE3 equivariant graph neural networks for accurate and efficient blood flow predictions in complex arterial structures. Gain insights into how this innovative technique leverages geometric deep learning to enhance computational fluid dynamics simulations in cardiovascular research and clinical applications.
Syllabus
SE3 equivariant hemodynamic field estimation in 3D artery models via graph neural networks
Taught by
Conference GSI
Related Courses
Graph Attention Networks - GNN Paper ExplainedAleksa Gordić - The AI Epiphany via YouTube Geometric Deep Learning for Drug Discovery
IEEE Signal Processing Society via YouTube Detection of Objects in Cryo-Electron Micrographs Using Geometric Deep Learning
Institute for Pure & Applied Mathematics (IPAM) via YouTube Physics-Inspired Learning on Graph - Michael Bronstein, PhD
Open Data Science via YouTube Inverse Problems on Graphs with Geometric Deep Learning
APS Physics via YouTube