Manifold Learning - David Ryckelynck
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Présentation Manifold Learning de David Ryckelynck Format Broché
- Livre Loisirs
Résumé :
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....
Biographie:
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 Ing?nierie Digitale Des Systemes Complexes (Data Science for Computational Engineering) at Mines Paris PSL University.
Sommaire:
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.