Manifold Learning

Model Reduction in Engineering
2024
ISBN: 978-3-031-52766-1
Verlag: Springer Nature Switzerland

Buch, Englisch, 107 Seiten, Paperback, Format (B × H): 155 mm x 235 mm, Gewicht: 195 g

Reihe: 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.

Zielgruppe

Research

Autoren/Hrsg.

Ryckelynck, David

Akkari, Nissrine

Casenave, Fabien

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Stochastische Prozesse
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Produktionstechnik
• Technische Wissenschaften Maschinenbau | Werkstoffkunde Technische Mechanik | Werkstoffkunde Technische Thermodynamik
• Naturwissenschaften Physik Physik Allgemein Theoretische Physik, Mathematische Physik, Computerphysik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen

Inhaltsverzeichnis

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.

Über Autor(innen)

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.