國立虎尾科技大學 |

The Calabi–Yau Landscape = From Geometry, to Physics, to Machine Learning /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	The Calabi–Yau Landscape/ by Yang-Hui He.
其他題名:	From Geometry, to Physics, to Machine Learning /
作者:	He, Yang-Hui.
面頁冊數:	XVII, 206 p. 36 illus., 26 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Mathematical Software. -
電子資源:	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. - 1st ed. 2021. - XVII, 206 p. 36 illus., 26 illus. in color.online resource. - Lecture Notes in Mathematics,22931617-9692 ;. - Lecture Notes in Mathematics,2144.

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:

672446
Mathematical Software.

LC Class. No.: QA564-609

Dewey Class. No.: 516.35

The Calabi–Yau Landscape = From Geometry, to Physics, to Machine Learning /
LDR:02862nam a22003975i 4500 001 1051135
003 DE-He213
005 20210904071151.0
007 cr nn 008mamaa
008 220103s2021 sz | s |||| 0|eng d
020 $a 9783030775629 $9 978-3-030-77562-9
024 7 $a 10.1007/978-3-030-77562-9 $2 doi
035 $a 978-3-030-77562-9
050 4 $a QA564-609
072 7 $a PBMW $2 bicssc
072 7 $a MAT012010 $2 bisacsh
072 7 $a PBMW $2 thema
082 0 4 $a 516.35 $2 23
100 1 $a He, Yang-Hui. $e author. $0 (orcid)0000-0002-0787-8380 $1 https://orcid.org/0000-0002-0787-8380 $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1355622
245 1 4 $a The Calabi–Yau Landscape $h [electronic resource] : $b From Geometry, to Physics, to Machine Learning / $c by Yang-Hui He.
250 $a 1st ed. 2021.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2021.
300 $a XVII, 206 p. 36 illus., 26 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Lecture Notes in Mathematics, $x 1617-9692 ; $v 2293
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.
650 2 4 $a Mathematical Software. $3 672446
650 2 4 $a Machine Learning. $3 1137723
650 2 4 $a Mathematical Physics. $3 786661
650 1 4 $a Algebraic Geometry. $3 670184
650 0 $a Computer software. $3 528062
650 0 $a Machine learning. $3 561253
650 0 $a Mathematical physics. $3 527831
650 0 $a Algebraic geometry. $3 1255324
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030775612
776 0 8 $i Printed edition: $z 9783030775636
830 0 $a Lecture Notes in Mathematics, $x 0075-8434 ; $v 2144 $3 1254300
856 4 0 $u https://doi.org/10.1007/978-3-030-77562-9
912 $a ZDB-2-SMA
912 $a ZDB-2-SXMS
912 $a ZDB-2-LNM
950 $a Mathematics and Statistics (SpringerNature-11649)
950 $a Mathematics and Statistics (R0) (SpringerNature-43713)