000			03438namaa2200469uu 4500
001			doab135558
003			oapen
005			20260218110447.0
006			m o d
007			cr|mn|---annan
008			240314s2024 xx |||||o ||| 0|eng d
020			_a978-3-031-52764-7
020			_a9783031527630
020			_a9783031527647
024	7		_a10.1007/978-3-031-52764-7 _2doi
040			_aoapen _coapen _d
041	0		_aeng
042			_adc
720	1		_aRyckelynck, David _4aut
245	0	0	_aManifold Learning _bModel Reduction in Engineering
260			_aCham _bSpringer Nature _c2024
300			_a1 online resource (107 p.)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aSpringerBriefs in Computer Science
506	0		_fUnrestricted online access _2star
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.
540			_aCreative Commons _fby/4.0/ _2cc _uhttp://creativecommons.org/licenses/by/4.0/
546			_aEnglish
650		7	_aEngineering thermodynamics _2bicssc
650		7	_aMachine learning _2bicssc
650		7	_aMathematical and statistical software _2bicssc
650		7	_aMathematical physics _2bicssc
650		7	_aProbability and statistics _2bicssc
650		7	_aProduction and industrial engineering _2bicssc
720	1		_aAkkari, Nissrine _4aut
720	1		_aCasenave, Fabien _4aut
793	0		_aDOAB Library.
856			_uhttps://docs.google.com/spreadsheets/d/1yKIrdCPDAG_9c22mwoOIO2DOhtj65Wqa/edit?usp=sharing&ouid=106555315294820607512&rtpof=true&sd=true _yList of Curated E-Books
942			_cE-BOOK
999			_c81349 _d81348