000			03422nam a2200409 a 4500
001			000q0404
003			WSP
005			20260416153408.0
007			cr |nu|||unuuu
008			230114s2023 enk ob 001 0 eng d
010			_a 2022058911
020			_a9781800613706 _q(ebook)
020			_a1800613709 _q(ebook)
020			_z9781800613690 _q(hbk.)
040			_aWSPC _beng _cWSPC
050		4	_aQ325.5
072		7	_aCOM _x094000 _2bisacsh
072		7	_aMAT _x000000 _2bisacsh
072		7	_aSCI _x055000 _2bisacsh
082	0	4	_a006.3/1015 _223
049			_aMAIN
245	0	0	_aMachine learning in pure mathematics and theoretical physics / _cedited by Yang-Hui He.
260			_aLondon : _bWorld Scientific Publishing Europe Ltd., _cc2023.
300			_a1 online resource (xxii, 395 p.)
504			_aIncludes bibliographical references and index.
505	0		_aMachine learning meets number theory : the data science of Birch-Swinnerton-Dyer -- On the dynamics of inference and learning -- Machine learning : the dimension of a polytope -- Intelligent explorations of the string theory landscape -- Deep learning : complete intersection Calabi-Yau manifolds -- Deep-learning the landscape -- hep-th -- Symmetry-via-duality : invariant neural network densities from parameter-space correlators -- Supervised learning of arithmetic invariants -- Calabi-Yau volumes, reflexive polytopes and machine learning.
520			_a"The juxtaposition of "machine learning" and "pure mathematics and theoretical physics" may first appear as contradictory in terms. The rigours of proofs and derivations in the latter seem to reside in a different world from the randomness of data and statistics in the former. Yet, an often under-appreciated component of mathematical discovery, typically not presented in a final draft, is experimentation: both with ideas and with mathematical data. Think of the teenage Gauss, who conjectured the Prime Number Theorem by plotting the prime-counting function, many decades before complex analysis was formalized to offer a proof. Can modern technology in part mimic Gauss's intuition? The past five years saw an explosion of activity in using AI to assist the human mind in uncovering new mathematics: finding patterns, accelerating computations, and raising conjectures via the machine learning of pure, noiseless data. The aim of this book, a first of its kind, is to collect research and survey articles from experts in this emerging dialogue between theoretical mathematics and machine learning. It does not dwell on the well-known multitude of mathematical techniques in deep learning, but focuses on the reverse relationship: how machine learning helps with mathematics. Taking a panoramic approach, the topics range from combinatorics to number theory, and from geometry to quantum field theory and string theory. Aimed at PhD students as well as seasoned researchers, each self-contained chapter offers a glimpse of an exciting future of this symbiosis"-- _cPublisher's website.
538			_aMode of access: World Wide Web.
538			_aSystem requirements: Adobe Acrobat Reader.
650		0	_aMachine learning.
650		0	_aMathematics _xData processing.
650		0	_aPhysics _xData processing.
655		0	_aElectronic books.
700	1		_aHe, Yang-Hui, _d1975-
856	4	0	_uhttps://www.worldscientific.com/worldscibooks/10.1142/q0404#t=toc
942			_cE-BOOK
999			_c49727 _d49727