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    Manifold Learning

    Model Reduction in Engineering

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    Author(s)
    Ryckelynck, David
    Casenave, Fabien
    Akkari, Nissrine
    Language
    English
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    Abstract
    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.
    URI
    https://library.oapen.org/handle/20.500.12657/88364
    Keywords
    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
    DOI
    10.1007/978-3-031-52764-7
    ISBN
    9783031527647, 9783031527630, 9783031527647
    Publisher
    Springer Nature
    Publisher website
    https://www.springernature.com/gp/products/books
    Publication date and place
    Cham, 2024
    Imprint
    Springer Nature Switzerland
    Series
    SpringerBriefs in Computer Science,
    Classification
    Machine learning
    Mathematical and statistical software
    Probability and statistics
    Engineering thermodynamics
    Production and industrial engineering
    Mathematical physics
    Pages
    107
    Rights
    http://creativecommons.org/licenses/by/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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