| 书目名称 | Manifold Learning |
| 副标题 | Model Reduction in E |
| 编辑 | David Ryckelynck,Fabien Casenave,Nissrine Akkari |
| 视频video | |
| 概述 | 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 i |
| 丛书名称 | SpringerBriefs in Computer Science |
| 图书封面 |  |
| 描述 | 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 |
| 出版日期 | Book‘‘‘‘‘‘‘‘ 2024 |
| 关键词 | Computational Mechanics; Data Augmentation; Deep Learning; Digital Twining; Dimensionality Reduction; Gen |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-031-52764-7 |
| isbn_softcover | 978-3-031-52766-1 |
| isbn_ebook | 978-3-031-52764-7Series ISSN 2191-5768 Series E-ISSN 2191-5776 |
| issn_series | 2191-5768 |
| copyright | The Editor(s) (if applicable) and The Author(s) 2024 |
发表于 2025-3-21 19:01:00
