Nash Embedding Theorem, Geometric Deep Learning, My field prize prediction, Predicting N-Body dynamic systems & Network Science..

Hello,

There has been lot of buzz around energy based AI models & Geometric deep learning since my comments in Forbes. I have also been speaking a lot about geometric deep learning for last few years..  

A very nice paper on it - Geometric deep learning: going beyond Euclidean data - https://arxiv.org/pdf/1611.08097.pdf 

The reasons I am mentioning this paper which includes Nash Embedding Theorem, Geodesics, Graph neural networks etc. are 

1. Field Prize Prediction - While I know my field prize prediction came true with 15 different algorithms, NLP and analyzing last 50 years of research papers, The example of Network graph across co-author & their publications in this paper could have been possibly a faster way to predict the field prize (The use of Nash Embedding Theorem, Geometric Deep Learning and Graph neural Networks is very cool way of analyzing the co-author network).. 

2. Predicting N-Body Systems & Quantum - Such Graph neural networks are currently being applied to perform event classification, energy regression, and anomaly detection in high-energy physics experiments such as the Large Hadron Collider (LHC) and neutrino detection in the IceCube Observatory. Recently, models based on graph neural networks have been applied to predict the dynamics of N-body systems  showing excellent prediction performance.

One application of GNN, Geometric Deep Learning which could have been a very cool collaboration is the new paper on Double-superradiant cathodoluminescence - Technion - Israel Institute of Technology, University of Cambridge & Harvard University - https://arxiv.org/pdf/2209.05876.pdf