A neural network approach to predicting and computing knot invariants

Understand

In this paper we use artificial neural networks to predict and help compute the values of certain knot invariants.

• In particular, we show that neural networks are able to predict when a knot is quasipositive with a high degree of accuracy.
• Given a knot with unknown quasipositivity we use these predictions to identify braid representatives that are likely to be quasipositive, which we then subject to further testing to verify.
• Using these techniques we identify 84 new quasipositive 11 and 12-crossing knots.