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...

Full description

Bibliographic Details
Main Author:	Ryckelynck, David (-)
Other Authors:	Casenave, Fabien, Akkari, Nissrine
Format:	eBook
Language:	Inglés
Published:	Cham : Springer Nature Switzerland 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.
See on Biblioteca Universitat Ramon Llull:	https://discovery.url.edu/permalink/34CSUC_URL/1im36ta/alma991009805100906719

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.

Manifold Learning Model Reduction in Engineering

Similar Items