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008			210805s2021 sz | s |||| 0|eng d
020			_a9783030765873
024	7		_a10.1007/978-3-030-76587-3 _2doi
040			_aES-MaUEC _bspa _cES-MaUEC _dES-MaUEC
050		4	_aQ325.5 _b2021 EB
100			_aKollmannsberger, Stefan _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9682187
245	1	0	_aDeep Learning in Computational Mechanics : _bAn Introductory Course _cby Stefan Kollmannsberger, Davide D'Angella, Moritz Jokeit, Leon Herrmann.
250			_aFirst edition 2021
264		1	_aCham _bSpringer International Pulishing _c2021
300			_a1 recurso en línea (VI, 104 páginas) _b41 ilustraciones, 22 ilustraciones a color
336			_2rdacontent _aTexto _btxt
337			_2rdamedia _aelectrónico _bc
338			_2rdacarrier _arecurso electrónico _bcr
347			_aarchivo de texto _bPDF
490	0		_aStudies in Computational Intelligence _x1860-9503 _v977
490	0		_aIntelligent Technologies and Robotics (SpringerNature-42732)
490	0		_aIntelligent Technologies and Robotics (R0) (SpringerNature-43728)
505	0		_aIntroduction -- Fundamental Concepts of Machine Learning -- Neural Networks -- Machine Learning in Physics and Engineering -- Physics-informed Neural Networks -- Deep Energy Method.
520	3		_aThis book provides a first course on deep learning in computational mechanics. The book starts with a short introduction to machine learning's fundamental concepts before neural networks are explained thoroughly. It then provides an overview of current topics in physics and engineering, setting the stage for the book's main topics: physics-informed neural networks and the deep energy method. The idea of the book is to provide the basic concepts in a mathematically sound manner and yet to stay as simple as possible. To achieve this goal, mostly one-dimensional examples are investigated, such as approximating functions by neural networks or the simulation of the temperature's evolution in a one-dimensional bar. Each chapter contains examples and exercises which are either solved analytically or in PyTorch, an open-source machine learning framework for python.
988			_aSpringer_Robotics_2021
650		7	_2embne _9166090 _aAprendizaje automático
700			_aD'Angella, Davide _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9682188
700			_aJokeit, Moritz _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9682189
700			_aHerrmann, Leon _eautor _4aut _4http://id.loc.gov/vocabulary/relators/aut _9682191
776	0	8	_iPrinted edition: _z9783030765866
776	0	8	_iPrinted edition: _z9783030765880
776	0	8	_iPrinted edition: _z9783030765897
856	4	0	_uhttps://go.openathens.net/redirector/universidadeuropea.es?url=https://doi.org/10.1007/978-3-030-76587-3 _zAcceso a este recurso digital (usuarios Universidad Europea de Madrid)
942			_2lcc _cLE