论文标题
宇宙作为大数据
Universes as Big Data
论文作者
论文摘要
我们简要概述了弦理论如何使理论物理学首先导致代数和差异几何形状的精确问题，从而在过去十年左右的时间内将计算几何形状转化为计算几何形状，现在，在过去的几年中，到了数据科学。在过去的40年中，使用物理学家，数学家和计算机科学家的合作来累积的Calabi-Yau景观 - 作为起点和混凝土游乐场，我们回顾了机器学习的最新进展，适用于通过压缩中可能的宇宙筛分的筛分，以及在量子领域的地球工程中的更广泛的问题。同时，我们在机器学习数学结构中讨论了该程序，并解决了它如何帮助进行数学的诱人问题，从数学物理学到几何，几何，代表理论，再到组合理论，再到数字理论。
We briefly overview how, historically, string theory led theoretical physics first to precise problems in algebraic and differential geometry, and thence to computational geometry in the last decade or so, and now, in the last few years, to data science. Using the Calabi-Yau landscape -- accumulated by the collaboration of physicists, mathematicians and computer scientists over the last 4 decades -- as a starting-point and concrete playground, we review some recent progress in machine-learning applied to the sifting through of possible universes from compactification, as well as wider problems in geometrical engineering of quantum field theories. In parallel, we discuss the programme in machine-learning mathematical structures and address the tantalizing question of how it helps doing mathematics, ranging from mathematical physics, to geometry, to representation theory, to combinatorics, and to number theory.
