Manifold Learning

Model Reduction in Engineering

David Ryckelynck, Fabien Casenave, Nissrine Akkari

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English

Springer International Publishing AG
21 February 2024

Probability & statistics; Stochastics; Engineering thermodynamics; Production engineering; Machine learning

Series: SpringerBriefs in Computer Science

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.

By: David Ryckelynck, Fabien Casenave, Nissrine Akkari
Imprint: Springer International Publishing AG
Country of Publication: Switzerland
Edition: 1st ed. 2024
Dimensions: Height: 235mm, Width: 155mm,
Weight: 195g
ISBN: 9783031527661
ISBN 10: 3031527666
Series: SpringerBriefs in Computer Science
Pages: 107
Publication Date: 21 February 2024
Audience: Professional and scholarly , College/higher education , Undergraduate , Further / Higher Education
Format: Paperback
Publisher's Status: Active

Structured Data and Knowledge in Model-based Engineering.- Learning Projection-based Reduced-order Models.- Error Estimation.- Resources: Software and Tutorials.- Industrial Application: Uncertainty Quantification in Lifetime Prediction of Turbine Blades.- Applications and Extensions: A Survey of Literature.

David Ryckelynck is working on model-based/physics-based engineering assisted by machine learning. He did seminal works on hyper-reduction methods, in the field of applied mathematics and computational mechanics. He is the head of a lecture on Ingénierie Digitale Des Systemes Complexes (Data Science for Computational Engineering) at Mines Paris PSL University. Fabien Casenave is a research scientist at Safran Tech, the research center of Safran Group, a French multinational company that designs, develops and manufactures aircraft engines, rocket engines as well as various aerospace and defense-related equipment or their components. As head of the Physics-Informed AI and Numerical Experiments team, Fabien has been working on model-based/physics-based engineering assisted by machine learning applied to industrial design challenges in structural mechanics. Nissrine Akkari is a research scientist at Safran Tech. She has been working on model-based/physics-based engineering assisted by machine learning applied to industrial design challenges in computational fluid dynamics.