Advances in Applied Clifford Algebras - Call for Papers for Topical Collection: Machine-Learning Mathematical Structures
Guest Editors:
Yang-Hui He (London, Oxford, Tianjin) - hey@maths.ox.ac.uk (this opens in a new tab)
Pierre-Philippe Dechant (Leeds) - p.p.dechant@leeds.ac.uk (this opens in a new tab)
Alexander Kasprzyk (Nottingham) - A.M.Kasprzyk@nottingham.ac.uk (this opens in a new tab)
Andre Lukas (Oxford) - andre.lukas@physics.ox.ac.uk (this opens in a new tab)
There has been a host of activity in the last 2-3 years to use techniques from modern data science, such as machine learning, data mining, and semantic/linguistic analyses, to understand the fundamental structures of mathematics. In mathematical physics, this began with deep learning explorations of the string landscape. In algebraic geometry, supervised learning has been applied to computing topological invariants as well as finding efficient Groebner bases. In algebra and representation theory, combinatorics, and graph theory, neural classifiers and regressors have been used, for instance, to distinguish finite simple groups, to detect Hamiltonian cycles, and to estimate Laplacian eigenvalues. In symbolic manipulation, neural networks such as the Ramanujan Machine, SciNet, and statistical learning networks have been used to generate the likes of new partial fractions and integral identities.
It is clear that this emergent field will play a significant role in experimental mathematics, mathematical physics as well as conjecture formulation in pure mathematics across the disciplines. Such machine learning paradigms in mathematics should be in tandem with Voevodsky's dream of automated theorem proving.
Indeed, in light of the interests of this journal, machine learning patterns in Clifford and Grassmann algebras and beyond beckon immediate attention. In this topical collection (in line with an attempted summary of some of the recent progress in the plenary lecture at the 12th International Conference on Clifford Algebras and Their Applications in Mathematical Physics in 2020 (this opens in a new tab)), we invite experts working on the interface between machine learning and various branches of mathematics to contribute papers of high quality. We bear in mind the huge inter- and cross- disciplinary nature of this collection, and hope it will act as a new forum for dialogues between mathematicians, physicists, as well as computer and data scientists.
1. All submissions are expected to follow general submission guidelines for authors posted at
https://www.springer.com/journal/6/submission-guidelines (this opens in a new tab)
while a LaTeX class file with additional instructions for authors is posted at
https://www.springer.com/journal/6/updates/17956942 (this opens in a new tab)
2. Submissions should be made under article type to "T.C. Machine-learning Mathematical Structures" through AACA's submissions page https://www.editorialmanager.com/aaca/default.aspx (this opens in a new tab)
3. All submissions will be refereed while following general AACA standards.
4. Only well-developed papers of the highest scientific quality reporting newest results will be accepted. One or two review papers may be considered as well.
5. The submission is open.
Comment: Submissions accepted for publication in AACA will appear:
(a) online, approximately 15 days after the acceptance and
(b) in print, in the first available paper issue of AACA.
Click to download the Call for Papers (this opens in a new tab)