Evolving neural networks with genetic algorithms to study the string landscape
作者: Fabian Ruehle
作者单位: 1Oxford University
刊名: Journal of High Energy Physics, 2017, Vol.2017 (8)
来源数据库: Springer Nature Journal
DOI: 10.1007/JHEP08(2017)038
关键词: Superstring VacuaSuperstrings and Heterotic Strings
原始语种摘要: We study possible applications of artificial neural networks to examine the string landscape. Since the field of application is rather versatile, we propose to dynamically evolve these networks via genetic algorithms. This means that we start from basic building blocks and combine them such that the neural network performs best for the application we are interested in. We study three areas in which neural networks can be applied: to classify models according to a fixed set of (physically) appealing features, to find a concrete realization for a computation for which the precise algorithm is known in principle but very tedious to actually implement, and to predict or approximate the outcome of some involved mathematical computation which performs too inefficient to apply it, e.g. in model...
全文获取路径: Springer Nature  (合作)
影响因子:5.618 (2012)

  • neural 神经系统的
  • string 细绳
  • study 学习
  • genetic 遗传的
  • application 申请
  • approximate 近似的
  • physically 物理上
  • versatile 多方面的
  • computation 计算
  • features 特征