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oapen-20.500.12657-883642024-03-28T14:02:54Z Manifold Learning Ryckelynck, David Casenave, Fabien Akkari, Nissrine Computational Mechanics Data Augmentation Deep Learning Digital Twining Dimensionality Reduction GenericROM Library High-Fidelity Model Hyper-reduction Image-based Digital Twins Manifold Learning Model Order Reduction Mordicus Multiphysics Modeling thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UF Business applications::UFM Mathematical and statistical software thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGM Materials science::TGMB Engineering thermodynamics thema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TG Mechanical engineering and materials::TGP Production and industrial engineering thema EDItEUR::P Mathematics and Science::PH Physics::PHU Mathematical physics This 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. 2024-03-13T11:11:17Z 2024-03-13T11:11:17Z 2024 book ONIX_20240313_9783031527647_50 9783031527647 9783031527630 https://library.oapen.org/handle/20.500.12657/88364 eng SpringerBriefs in Computer Science application/pdf n/a 978-3-031-52764-7.pdf https://link.springer.com/978-3-031-52764-7 Springer Nature Springer Nature Switzerland 10.1007/978-3-031-52764-7 10.1007/978-3-031-52764-7 6c6992af-b843-4f46-859c-f6e9998e40d5 353756ce-e3d3-458b-9f84-4b0c578662ce 9783031527647 9783031527630 Springer Nature Switzerland 107 Cham [...] open access
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This 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.
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