Machine learning discovers invariants of braids and flat braids

07/22/2023
by   Alexei Lisitsa, et al.
0

We use machine learning to classify examples of braids (or flat braids) as trivial or non-trivial. Our ML takes form of supervised learning using neural networks (multilayer perceptrons). When they achieve good results in classification, we are able to interpret their structure as mathematical conjectures and then prove these conjectures as theorems. As a result, we find new convenient invariants of braids, including a complete invariant of flat braids.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
08/25/2022

Electronic Appendix to "Input Invariants"

In this electronic appendix to our paper "Input Invariants," accepted at...
research
12/07/2020

Machine-Learning Arithmetic Curves

We show that standard machine-learning algorithms may be trained to pred...
research
07/18/2021

Frobenius statistical manifolds geometric invariants

In this paper, we explicitly prove that statistical manifolds, related t...
research
05/29/2013

Rotation invariants of two dimensional curves based on iterated integrals

We introduce a novel class of rotation invariants of two dimensional cur...
research
09/13/2023

Flat origami is Turing Complete

Flat origami refers to the folding of flat, zero-curvature paper such th...
research
12/31/2020

Neural Network Approximations for Calabi-Yau Metrics

Ricci flat metrics for Calabi-Yau threefolds are not known analytically....
research
11/30/2021

Learning knot invariants across dimensions

We use deep neural networks to machine learn correlations between knot i...

Please sign up or login with your details

Forgot password? Click here to reset