Manifold Learning Model Reduction In Engineering - David Ryckelynck

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.