An introduction to computational complexity and statistical learning theory applied to nuclear models
Idini, Andrea LU
(2023)
28th International Nuclear Physics Conference, INPC 2022
In Journal of Physics: Conference Series
2586.
- Abstract
The fact that we can build models from data, and therefore refine our models with more data from experiments, is usually given for granted in scientific inquiry. However, how much information can we extract, and how precise can we expect our learned model to be, if we have only a finite amount of data at our disposal? Nuclear physics demands an high degree of precision from models that are inferred from the limited number of nuclei that can be possibly made in the laboratories. In manuscript I will introduce some concepts of computational science, such as statistical theory of learning and Hamiltonian complexity, and use them to contextualise the results concerning the amount of data necessary to extrapolate a mass model to a given... (More)
The fact that we can build models from data, and therefore refine our models with more data from experiments, is usually given for granted in scientific inquiry. However, how much information can we extract, and how precise can we expect our learned model to be, if we have only a finite amount of data at our disposal? Nuclear physics demands an high degree of precision from models that are inferred from the limited number of nuclei that can be possibly made in the laboratories. In manuscript I will introduce some concepts of computational science, such as statistical theory of learning and Hamiltonian complexity, and use them to contextualise the results concerning the amount of data necessary to extrapolate a mass model to a given precision.
(Less)
- author
- Idini, Andrea
LU
- organization
- publishing date
- 2023
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Journal of Physics: Conference Series
- series title
- Journal of Physics: Conference Series
- volume
- 2586
- edition
- 1
- conference name
- 28th International Nuclear Physics Conference, INPC 2022
- conference location
- Cape Town, South Africa
- conference dates
- 2022-09-11 - 2022-09-16
- external identifiers
-
- scopus:85174591366
- ISSN
- 1742-6588
- DOI
- 10.1088/1742-6596/2586/1/012155
- language
- English
- LU publication?
- yes
- id
- 449708e8-e908-48ec-adde-6372728c345a
- date added to LUP
- 2024-01-12 09:26:10
- date last changed
- 2024-01-12 09:27:44
@inproceedings{449708e8-e908-48ec-adde-6372728c345a,
abstract = {{<p>The fact that we can build models from data, and therefore refine our models with more data from experiments, is usually given for granted in scientific inquiry. However, how much information can we extract, and how precise can we expect our learned model to be, if we have only a finite amount of data at our disposal? Nuclear physics demands an high degree of precision from models that are inferred from the limited number of nuclei that can be possibly made in the laboratories. In manuscript I will introduce some concepts of computational science, such as statistical theory of learning and Hamiltonian complexity, and use them to contextualise the results concerning the amount of data necessary to extrapolate a mass model to a given precision.</p>}},
author = {{Idini, Andrea}},
booktitle = {{Journal of Physics: Conference Series}},
issn = {{1742-6588}},
language = {{eng}},
series = {{Journal of Physics: Conference Series}},
title = {{An introduction to computational complexity and statistical learning theory applied to nuclear models}},
url = {{http://dx.doi.org/10.1088/1742-6596/2586/1/012155}},
doi = {{10.1088/1742-6596/2586/1/012155}},
volume = {{2586}},
year = {{2023}},
}