Learning bridge numbers of knots
This work addresses knot theory problems for mathematicians and computational researchers, but it is incremental as it applies existing methods to new data.
The paper tackled the problem of determining bridge numbers for classical and virtual knots using computational techniques, finding that the difference in definitions for virtual knots can be arbitrarily large, and evaluated machine learning models on datasets of over one million labeled data points for classification.
This paper employs various computational techniques to determine the bridge numbers of both classical and virtual knots. For classical knots, there is no ambiguity of what the bridge number means. For virtual knots, there are multiple natural definitions of bridge number, and we demonstrate that the difference can be arbitrarily far apart. We then acquired two datasets, one for classical and one for virtual knots, each comprising over one million labeled data points. With the data, we conduct experiments to evaluate the effectiveness of common machine learning models in classifying knots based on their bridge numbers.