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The Calabi-Yau landscape = from geom...

He, Yang-Hui.

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The Calabi-Yau landscape = from geometry, to physics, to machine learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	The Calabi-Yau landscape/ by Yang-Hui He.
Reminder of title:	from geometry, to physics, to machine learning /
Author:	He, Yang-Hui.
Published:	Cham :Springer International Publishing : : 2021.,
Description:	xvii, 206 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Calabi-Yau manifolds. -
Online resource:	https://doi.org/10.1007/978-3-030-77562-9
ISBN:	9783030775629

The Calabi-Yau landscape = from geometry, to physics, to machine learning /
He, Yang-Hui.

The Calabi-Yau landscapefrom geometry, to physics, to machine learning /[electronic resource] :by Yang-Hui He. - Cham :Springer International Publishing :2021. - xvii, 206 p. :ill., digital ;24 cm. - Lecture notes in mathematics,v.22930075-8434 ;. - Lecture notes in mathematics ;v.2293..

Can artificial intelligence learn mathematics? The question is at the heart of this original monograph bringing together theoretical physics, modern geometry, and data science. The study of Calabi-Yau manifolds lies at an exciting intersection between physics and mathematics. Recently, there has been much activity in applying machine learning to solve otherwise intractable problems, to conjecture new formulae, or to understand the underlying structure of mathematics. In this book, insights from string and quantum field theory are combined with powerful techniques from complex and algebraic geometry, then translated into algorithms with the ultimate aim of deriving new information about Calabi-Yau manifolds. While the motivation comes from mathematical physics, the techniques are purely mathematical and the theme is that of explicit calculations. The reader is guided through the theory and provided with explicit computer code in standard software such as SageMath, Python and Mathematica to gain hands-on experience in applications of artificial intelligence to geometry. Driven by data and written in an informal style, The Calabi-Yau Landscape makes cutting-edge topics in mathematical physics, geometry and machine learning readily accessible to graduate students and beyond. The overriding ambition is to introduce some modern mathematics to the physicist, some modern physics to the mathematician, and machine learning to both.

ISBN: 9783030775629

Standard No.: 10.1007/978-3-030-77562-9doiSubjects--Topical Terms:

708642
Calabi-Yau manifolds.

LC Class. No.: QC20.7.M24 / H49 2021

Dewey Class. No.: 516.07

The Calabi-Yau landscape = from geometry, to physics, to machine learning /
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520 $a Can artificial intelligence learn mathematics? The question is at the heart of this original monograph bringing together theoretical physics, modern geometry, and data science. The study of Calabi-Yau manifolds lies at an exciting intersection between physics and mathematics. Recently, there has been much activity in applying machine learning to solve otherwise intractable problems, to conjecture new formulae, or to understand the underlying structure of mathematics. In this book, insights from string and quantum field theory are combined with powerful techniques from complex and algebraic geometry, then translated into algorithms with the ultimate aim of deriving new information about Calabi-Yau manifolds. While the motivation comes from mathematical physics, the techniques are purely mathematical and the theme is that of explicit calculations. The reader is guided through the theory and provided with explicit computer code in standard software such as SageMath, Python and Mathematica to gain hands-on experience in applications of artificial intelligence to geometry. Driven by data and written in an informal style, The Calabi-Yau Landscape makes cutting-edge topics in mathematical physics, geometry and machine learning readily accessible to graduate students and beyond. The overriding ambition is to introduce some modern mathematics to the physicist, some modern physics to the mathematician, and machine learning to both.
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