Manifold Learning Model Reduction in Engineering /

Manifold Learning Model Reduction in Engineering /

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 understandi...

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Bibliographic Details
Main Authors: Ryckelynck, David (Author), Casenave, Fabien (Author), Akkari, Nissrine (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic eBook
Language:English
Published: Cham : Springer Nature Switzerland : Imprint: Springer, 2024.
Edition:1st ed. 2024.
Series:SpringerBriefs in Computer Science,
Subjects:
Machine learning.
Stochastic models.
Thermodynamics.
Heat engineering.
Heat transfer.
Mass transfer.
Industrial engineering.
Production engineering.
Mathematical physics.
Machine Learning.
Statistical Learning.
Stochastic Modelling.
Engineering Thermodynamics, Heat and Mass Transfer.
Industrial and Production Engineering.
Mathematical Methods in Physics.
Online Access:Link to Metadata
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Table of Contents:
  • 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.

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