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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Detalles Bibliográficos
Autor principal: Ryckelynck, David (-)
Otros Autores: Casenave, Fabien, Akkari, Nissrine
Formato: Libro electrónico
Idioma:Inglés
Publicado: Cham : Springer Nature Switzerland 2024.
Edición:1st ed. 2024.
Colección:SpringerBriefs in Computer Science,
Materias:
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.
Ver en Biblioteca Universitat Ramon Llull:https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009805100906719
Tabla de Contenidos:
  • 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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