000			02954nam a22002657a 4500
003			OSt
005			20260701092050.0
008			260701b |||||||| |||| 00| 0 eng d
020			_a9781800613690
041			_aeng
082			_a006.31015 HE-M
245			_aMachine learning in pure mathematics and theoretical physics
260			_aSingapore; _bWorld Scientific Publishing Europe Ltd., _c2025.
300			_axii, 395p. ; 24 cm.
505			_aPreface -- Machine learning foundations -- Applications in pure mathematics -- Machine learning in algebra and geometry -- Number theory applications -- Computational methods in theoretical physics -- Quantum computing and AI -- Advanced research topics -- Bibliographical references.
520			_aThe 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.
520			_aThis edited volume presents recent advances in the application of machine learning to pure mathematics and theoretical physics. It covers mathematical data processing, artificial intelligence methods, computational modelling, pattern recognition, and modern research techniques that integrate machine learning with advanced mathematical and physical sciences.
650			_aMachine learning _986237
650			_aArtificial intelligence _986238
650			_aMathematics _xData processing _986239
650			_aPhysics _xData processing _986240
700			_aHe, Yang-Hui, ed. _986241
942			_cBK
999			_c200755 _d200755