000			02863cam a2200421M 4500
001			9781003201045
003			FlBoTFG
005			20240213122832.0
006			m o d
007			cr |n|||||||||
008			220820s2022 xx o 0|| 0 eng d
040			_aOCoLC-P _beng _cOCoLC-P
020			_a9781000635867 _q(electronic bk.)
020			_a1000635864 _q(electronic bk.)
020			_a9781003201045 _q(electronic bk.)
020			_a1003201040 _q(electronic bk.)
020			_a9781000635829 _q(electronic bk. : PDF)
020			_a1000635821 _q(electronic bk. : PDF)
020			_z1032061731
020			_z9781032061733
024	7		_a10.1201/9781003201045 _2doi
035			_a(OCoLC)1341394974
035			_a(OCoLC-P)1341394974
050		4	_aTA404.23
072		7	_aTEC _x021030 _2bisacsh
072		7	_aTGM _2bicssc
082	0	4	_a620.110285 _223
100	1		_aChakraborti, Nirupam.
245	1	0	_aDATA-DRIVEN EVOLUTIONARY MODELING IN MATERIALS TECHNOLOGY _h[electronic resource].
260			_a[S.l.] : _bCRC PRESS, _c2022.
300			_a1 online resource
520			_aDue to efficacy and optimization potential of genetic and evolutionary algorithms, they are used in learning and modeling especially with the advent of big data related problems. This book presents the algorithms and strategies specifically associated with pertinent issues in materials science domain. It discusses the procedures for evolutionary multi-objective optimization of objective functions created through these procedures and introduces available codes. Recent applications ranging from primary metal production to materials design are covered. It also describes hybrid modeling strategy, and other common modeling and simulation strategies like molecular dynamics, cellular automata etc. Features: Focuses on data-driven evolutionary modeling and optimization, including evolutionary deep learning. Include details on both algorithms and their applications in materials science and technology. Discusses hybrid data-driven modeling that couples evolutionary algorithms with generic computing strategies. Thoroughly discusses applications of pertinent strategies in metallurgy and materials. Provides overview of the major single and multi-objective evolutionary algorithms. This book aims at Researchers, Professionals, and Graduate students in Materials Science, Data-Driven Engineering, Metallurgical Engineering, Computational Materials Science, Structural Materials, and Functional Materials.
588			_aOCLC-licensed vendor bibliographic record.
650		0	_aMaterials science _xData processing.
650		0	_aMaterials science _xMathematical models.
856	4	0	_3Taylor & Francis _uhttps://www.taylorfrancis.com/books/9781003201045
856	4	2	_3OCLC metadata license agreement _uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999			_c6035 _d6035