000			03377 a2200421 4500
001			24004923
003			0199339
005			20251016151005.0
008			250125s2025 flu b 000 0 eng
010			_a 2024045874
020			_a9781032508856
020			_a9781032508870
020			_z9781003400165
035			_a24004923
040			_aLBSOR _beng _erda _cLBSOR _dDLC
042			_apcc
050	0	0	_aTA658.8 _b.C43 2025
082	0	0	_a624.1/7713028563 _223/eng/20250210
100	1		_aChallapalli, Adithya, _eauthor.
245	1	0	_aArtificial intelligence assisted structural optimization / _cAdithya Challapalli and Guoqiang Li.
250			_aFirst edition.
263			_a2503
264		1	_aBoca Raton : _bCRC Press, _c2025.
300			_axi, 208p.:
336			_atext _btxt _2rdacontent
337			_aunmediated _bn _2rdamedia
338			_avolume _bnc _2rdacarrier
504			_aIncludes bibliographical references.
505	0		_aIntroduction to structures with complex geometrical configurations -- Structural optimization -- Introduction to machine learning assisted structural optimization -- Structural optimization of biomimetic rods using machine learning regression -- Structural optimization of lattice structures -- Inverse machine learning using generative adversarial networks -- Design and optimization of mechanical metamaterials using correlation analysis data generation and fingerprinting of thin-walled cellular unit cells -- Summary and future perspectives.
520			_a"Artificial Intelligence Assisted Structural Optimization explores the use of machine learning and correlation analysis within the forward design and inverse design frameworks to design and optimize lightweight load bearing structures as well as mechanical metamaterials. Discussing both machine learning and design analysis in detail, this book enables readers to optimize their designs using a data driven approach. This book discusses the basics of the materials utilized, for example shape memory polymers, and the manufacturing approach employed, such as 3D or 4D printing. Additionally, the book discusses the use of forward design and inverse design frameworks to discover novel lattice unit cells and thin-walled cellular unit cells with enhanced mechanical and functional properties such as increased mechanical strength, heightened natural frequency, strengthened impact tolerance, and improved recovery stress. Inverse design methodologies using generative adversarial networks are proposed to further investigate and improve these structures. Detailed discussions on fingerprinting approaches, machine learning models, structure screening techniques and typical Python codes are provided in the book. The book provides detailed guidance for both students and industry engineers to optimize their structural designs using machine learning"-- _cProvided by publisher.
650		0	_aStructural optimization _xData processing.
650		0	_aArtificial intelligence _xIndustrial applications.
700	1		_aLi, Guoqiang, _d1965- _eauthor.
776	0	8	_iOnline version: _aChallapalli, Adithya. _tArtificial intelligence assisted structural optimization. _bFirst edition _dBoca Raton : CRC Press, 2025 _z9781003400165 _w(DLC) 2024045875
906			_a7 _bcbc _corignew _d1 _eecip _f20 _gy-gencatlg
942			_2lcc _cBK
999			_c43849 _d43849