Machine learning and serving of discrete field theories

A method for machine learning and serving of discrete field theories in physics is developed. The learning algorithm trains a discrete field theory from a set of observational data on a spacetime lattice, and the serving algorithm uses the learned discrete field theory to predict new observations of...

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Vydáno v:Scientific reports Ročník 10; číslo 1; s. 19329
Hlavní autor: Qin, Hong
Médium: Journal Article
Jazyk:angličtina
Vydáno: London Nature Publishing Group UK 09.11.2020
Nature Publishing Group
Témata:
639/705/1046
639/766/259
Algorithms
Artificial intelligence
Differential equations
Humanities and Social Sciences
information theory and computation
Learning algorithms
Machine learning
MATHEMATICS AND COMPUTING
multidisciplinary
Oscillations
Science
Science (multidisciplinary)
scientific data
Theory of relativity
ISSN:2045-2322, 2045-2322
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Shrnutí:A method for machine learning and serving of discrete field theories in physics is developed. The learning algorithm trains a discrete field theory from a set of observational data on a spacetime lattice, and the serving algorithm uses the learned discrete field theory to predict new observations of the field for new boundary and initial conditions. The approach of learning discrete field theories overcomes the difficulties associated with learning continuous theories by artificial intelligence. The serving algorithm of discrete field theories belongs to the family of structure-preserving geometric algorithms, which have been proven to be superior to the conventional algorithms based on discretization of differential equations. The effectiveness of the method and algorithms developed is demonstrated using the examples of nonlinear oscillations and the Kepler problem. In particular, the learning algorithm learns a discrete field theory from a set of data of planetary orbits similar to what Kepler inherited from Tycho Brahe in 1601, and the serving algorithm correctly predicts other planetary orbits, including parabolic and hyperbolic escaping orbits, of the solar system without learning or knowing Newton’s laws of motion and universal gravitation. The proposed algorithms are expected to be applicable when the effects of special relativity and general relativity are important.
Bibliografie:ObjectType-Article-1
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AC02-09CH11466
USDOE
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-020-76301-0