• Title: Manifold Learning: Model Reduction in Engineering
• Author(s) David Ryckelynck, Fabien Casenave, Nissrine Akkari
• Publisher: Springer; 1st ed. 2024 edition (March 23, 2024); eBook (Creative Commons Licensed)
• License(s): Creative Commons License (CC)
• Hardcover/Paperback: 120 pages
• eBook: PDF
• Language: English
• ISBN-10: 3031527666
• ISBN-13: 978-3031527661
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Book Description

The aim 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.

• David Ryckelynck is the head of a lecture on Ingénierie Digitale Des Systemes Complexes (Data Science for Computational Engineering) at Mines Paris PSL University.

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• Deep Learning and Neural Networks
• Physics, Computational Physics, and Mathematical Physics
• Machine Learning
• Data Analysis and Data Mining

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