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  • Model Reduction in Engineering
    David Ryckelynck; Fabien Casenave; Nissrine Akkari
    978-3-031-52764-7
    2024
    Edition 1
    • Shows how manifold learning uses model order reduction and deep learning for training models in continuum mechanics
    • Discusses high dimensional input variables in mechanical models, in particular for image-based digital twining
    • Proposes practical techniques such as data augmentation or hyper-reduction in order to reduce high dimensional models
    • This book is open access, which means that you have free and unlimited access

    Open Access

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