000			04617nam a22006855i 4500
001			978-3-031-52764-7
003			DE-He213
005			20240507154521.0
007			cr nn 008mamaa
008			240220s2024 sz | s |||| 0|eng d
020			_a9783031527647 _9978-3-031-52764-7
024	7		_a10.1007/978-3-031-52764-7 _2doi
050		4	_aQ325.5-.7
072		7	_aUYQM _2bicssc
072		7	_aMAT029000 _2bisacsh
072		7	_aUYQM _2thema
082	0	4	_a006.31 _223
100	1		_aRyckelynck, David. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
245	1	0	_aManifold Learning _h[electronic resource] : _bModel Reduction in Engineering / _cby David Ryckelynck, Fabien Casenave, Nissrine Akkari.
250			_a1st ed. 2024.
264		1	_aCham : _bSpringer Nature Switzerland : _bImprint: Springer, _c2024.
300			_aX, 107 p. 31 illus., 25 illus. in color. _bonline resource.
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
347			_atext file _bPDF _2rda
490	1		_aSpringerBriefs in Computer Science, _x2191-5776
505	0		_aStructured Data and Knowledge in Model-based Engineering -- Learning Projection-based Reduced-order Models -- Error Estimation -- Resources: Software and Tutorials -- Industrial Application: Uncertainty Quantification in Lifetime Prediction of Turbine Blades -- Applications and Extensions: A Survey of Literature.
506	0		_aOpen Access
520			_aThis Open Access book reviews recent theoretical and numerical developments in nonlinear model order reduction in continuum mechanics, being addressed to Master and PhD students, as well as to researchers, lecturers and instructors. The aim of the authors is to provide tools for a better understanding and implement reduced order models by using: physics-based models, synthetic data forecast by these models, experimental data and deep learning algorithms. The book involves a survey of key methods of model order reduction applied to model-based engineering and digital twining, by learning linear or nonlinear latent spaces. Projection-based reduced order models are the projection of mechanical equations on a latent space that have been learnt from both synthetic data and experimental data. Various descriptions and representations of structured data for model reduction are presented in the applications and survey chapters. Image-based digital twins are developed in a reduced setting. Reduced order models of as-manufactured components predict the mechanical effects of shape variations. A similar workflow is extended to multiphysics or coupled problems, with high dimensional input fields. Practical techniques are proposed for data augmentation and also for hyper-reduction, which is a key point to speed up projection-based model order reduction of finite element models. The book gives access to python libraries available on gitlab.com, which have been developed as part of the research program [FUI-25] MORDICUS funded by the French government. Similarly to deep learning for computer vision, deep learning for model order reduction circumvents the need to design parametric problems prior reducing models. Such an approach is highly relevant for image-base modelling or multiphysics modelling.
650		0	_aMachine learning.
650		0	_aStochastic models.
650		0	_aThermodynamics.
650		0	_aHeat engineering.
650		0	_aHeat transfer.
650		0	_aMass transfer.
650		0	_aIndustrial engineering.
650		0	_aProduction engineering.
650		0	_aMathematical physics.
650	1	4	_aMachine Learning.
650	2	4	_aStatistical Learning.
650	2	4	_aStochastic Modelling.
650	2	4	_aEngineering Thermodynamics, Heat and Mass Transfer.
650	2	4	_aIndustrial and Production Engineering.
650	2	4	_aMathematical Methods in Physics.
700	1		_aCasenave, Fabien. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
700	1		_aAkkari, Nissrine. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut
710	2		_aSpringerLink (Online service)
773	0		_tSpringer Nature eBook
776	0	8	_iPrinted edition: _z9783031527630
776	0	8	_iPrinted edition: _z9783031527654
776	0	8	_iPrinted edition: _z9783031527661
830		0	_aSpringerBriefs in Computer Science, _x2191-5776
856	4	0	_uhttps://doi.org/10.1007/978-3-031-52764-7
912			_aZDB-2-SCS
912			_aZDB-2-SXCS
912			_aZDB-2-SOB
999			_c37474 _d37474