# Learning to unknot

@article{Gukov2021LearningTU, title={Learning to unknot}, author={Sergei Gukov and James Halverson and Fabian Ruehle and Piotr Sulkowski}, journal={Machine Learning: Science and Technology}, year={2021}, volume={2} }

We introduce natural language processing into the study of knot theory, as made natural by the braid word representation of knots. We study the UNKNOT problem of determining whether or not a given knot is the unknot. After describing an algorithm to randomly generate N-crossing braids and their knot closures and discussing the induced prior on the distribution of knots, we apply binary classification to the UNKNOT decision problem. We find that the Reformer and shared-QK Transformer network… Expand

#### 11 Citations

Heterotic String Model Building with Monad Bundles and Reinforcement Learning

- Computer Science, Physics
- ArXiv
- 2021

By focusing on two specific manifolds with Picard numbers two and three, it is shown that reinforcement learning can be used successfully to explore monad bundles and hundreds of new candidate standard models are found. Expand

Performance of the Uniform Closure Method for open knotting as a Bayes-type classifier

- Mathematics
- 2020

The discovery of knotting in proteins and other macromolecular chains has motivated researchers to more carefully consider how to identify and classify knots in open arcs. Most definitions classify… Expand

Particle Physics Model Building with Reinforcement Learning

- Physics
- 2021

In this paper, we apply reinforcement learning to particle physics model building. As an example environment, we use the space of Froggatt-Nielsen type models for quark masses. Using a basic… Expand

Quark Mass Models and Reinforcement Learning

- Physics
- Journal of High Energy Physics
- 2021

Abstract
In this paper, we apply reinforcement learning to the problem of constructing models in particle physics. As an example environment, we use the space of Froggatt-Nielsen type models for… Expand

Untangling Braids with Multi-agent Q-Learning

- Computer Science, Mathematics
- ArXiv
- 2021

The results provide evidence that the more the system is trained, the better the untangling player gets at untangling braids, and at the same time, the tangling player produces good examples of tangled braids. Expand

Neural Network Approximations for Calabi-Yau Metrics

- Computer Science, Physics
- ArXiv
- 2020

Vishnu Jejjalaa , Damián Kaloni Mayorga Peñaa,b , Challenger Mishrac Mandelstam Institute for Theoretical Physics, School of Physics, NITheP, and CoE-MaSS, University of the Witwatersrand,… Expand

Universes as big data

- Physics, Mathematics
- International Journal of Modern Physics A
- 2021

In this paper, we briefly overview how, historically, string theory led theoretical physics first to precise problems in algebraic and differential geometry, and thence to computational geometry in… Expand

Topological Link Models of Multipartite Entanglement

- Physics
- 2021

We introduce a novel model of multipartite entanglement based on topological links, generalizing the graph/hypergraph entropy cone program. We demonstrate that there exist link representations of… Expand

Machine-Learning Mathematical Structures

- Computer Science, Physics
- ArXiv
- 2021

Focusing on supervised machine-learning on labeled data from different fields ranging from geometry to representation theory, from combinatorics to number theory, a comparative study of the accuracies on different problems is presented. Expand

Disentangling a Deep Learned Volume Formula

- Physics, Computer Science
- ArXiv
- 2020

A simple phenomenological formula is presented which approximates the hyperbolic volume of a knot using only a single evaluation of its Jones polynomial at a root of unity using an analytically continued Chern-Simons integration cycle. Expand

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