000 | 04644nam a22006975i 4500 | ||
---|---|---|---|
001 | 978-3-031-52764-7 | ||
003 | DE-He213 | ||
005 | 20240423130323.0 | ||
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 | ||
942 | _cSPRINGER | ||
999 |
_c187341 _d187341 |