# Machine-Learning Number Fields

@article{He2020MachineLearningNF, title={Machine-Learning Number Fields}, author={Yanghui He and K. Lee and T. Oliver}, journal={arXiv: Number Theory}, year={2020} }

We show that standard machine-learning algorithms may be trained to predict certain invariants of algebraic number fields to high accuracy. A random-forest classifier that is trained on finitely many Dedekind zeta coefficients is able to distinguish between real quadratic fields with class number 1 and 2, to 0.96 precision. Furthermore, the classifier is able to extrapolate to fields with discriminant outside the range of the training data. When trained on the coefficients of definingâ€¦Â CONTINUE READING

One Citation

#### References

SHOWING 1-10 OF 26 REFERENCES

Learning Algebraic Structures: Preliminary Investigations

- Computer Science, Mathematics
- ArXiv
- 2019

- 12
- PDF

Machine Learning meets Number Theory: The Data Science of Birch-Swinnerton-Dyer

- Mathematics, Computer Science
- ArXiv
- 2019

- 11
- PDF

A course in computational algebraic number theory

- Computer Science, Mathematics
- Graduate texts in mathematics
- 1993

- 2,603

The Calabi-Yau Landscape: from Geometry, to Physics, to Machine-Learning

- Physics, Mathematics
- 2018

- 31
- PDF