Published December 2017
| Published + Submitted
Journal Article
Open
Artificial neural network in cosmic landscape
- Creators
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Liu, Junyu
Abstract
In this paper we propose that artificial neural network, the basis of machine learning, is useful to generate the inflationary landscape from a cosmological point of view. Traditional numerical simulations of a global cosmic landscape typically need an exponential complexity when the number of fields is large. However, a basic application of artificial neural network could solve the problem based on the universal approximation theorem of the multilayer perceptron. A toy model in inflation with multiple light fields is investigated numerically as an example of such an application.
Additional Information
© 2017 Springer. This article is distributed under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited. Article funded by SCOAP3. Received July 21, 201; revised November 9, 2017; accepted December 22, 2017; published December 28, 2017. JL thanks the instructor Zejun Ding for his help and encouragement when some of ideas are presented in a final presentation of a course project of his course Computational Physics A in 2014 fall, when the author was a college student in the University of Science and Technology of China. We thank Sean Carroll, Ashmeet Singh and Yi Wang for their communications, Hao Xu for his valuable supports on computer science and programming, and Jiahui Liu for her discussions and initial collaboration on this project. JL is supported in part by the Walter Burke Institute for Theoretical Physics in California Institute of Technology, by U.S. Department of Energy, Office of Science, Office of High Energy Physics, under Award Number DE-SC0011632.Attached Files
Published - 10.1007_2FJHEP12_2017_149.pdf
Submitted - 1707.02800.pdf
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10.1007_2FJHEP12_2017_149.pdf
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Additional details
- Eprint ID
- 84071
- Resolver ID
- CaltechAUTHORS:20180103-152023970
- Walter Burke Institute for Theoretical Physics, Caltech
- DE-SC0011632
- Department of Energy (DOE)
- SCOAP3
- Created
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2018-01-03Created from EPrint's datestamp field
- Updated
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2021-11-15Created from EPrint's last_modified field
- Caltech groups
- Walter Burke Institute for Theoretical Physics