Artificial Neural Network in Cosmic Landscape
Fuente:
arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2017
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| _version_ | 1866912850037964800 |
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| author | Liu, Junyu |
| author_facet | Liu, Junyu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1707_02800 |
| institution | arXiv |
| publishDate | 2017 |
| record_format | arxiv |
| spellingShingle | Artificial Neural Network in Cosmic Landscape Liu, Junyu High Energy Physics - Theory Cosmology and Nongalactic Astrophysics Artificial Intelligence Machine Learning General Relativity and Quantum Cosmology 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. |
| title | Artificial Neural Network in Cosmic Landscape |
| topic | High Energy Physics - Theory Cosmology and Nongalactic Astrophysics Artificial Intelligence Machine Learning General Relativity and Quantum Cosmology |
| url | https://arxiv.org/abs/1707.02800 |