论文标题
应用于核模型的计算复杂性和统计学习理论的简介
An introduction to computational complexity and statistical learning theory applied to nuclear models
论文作者
论文摘要
我们可以从数据中构建模型，因此可以通过实验中的更多数据来改进模型，这一事实通常是在科学探究中授予的。但是，如果我们只有有限的数据，我们可以提取多少信息，以及我们可以期望我们学到的模型是多么精确？核物理学要求从实验室中可能制造的有限数量的核中推断出的模型可以高度准确。 在手稿中，我将介绍一些计算科学的概念，例如学习和哈密顿复杂性的统计理论，并利用它们将有关将质量模型推送到给定精度所需的数据量的结果进行环境化。
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.
