### Reduced Basis Methods for Computational Mechanics

Mathematics Area, Mathematical Analysis, Modelling and Applications,
Course on Advanced Topic in Numerical Modelling PDEs

Part I: Reduced Basis Methods for Computational Mechanics, 15-17 March 2016, SISSA Room D, campus Miramare (Strada Costiera)

Lecturer Prof. Gianluigi Rozza, Tutorials coordinated by Dr Francesco Ballarin and Dr Alberto Sartori
Learning outcomes/Objectives:
The course aims to provide the basic aspects of numerical approximation and efficient solution of parametrized PDEs for computational mechanics problem (heat and mass transfer, linear elasticity, viscous and potential flows) using reduced order methods.
Module Description:
In this course we present reduced basis (RB) approximation and associated a posteriori error estimation for rapid and reliable solution of parametrized partial differential equations (PDEs). The focus is on rapidly convergent Galerkin approximations on a subspace spanned by snapshots''; rigorous and sharp a posteriori error estimators for the outputs/quantities of interest; efficient selection of quasi-optimal samples in general parameter domains; and Offline-Online computational procedures for rapid calculation in the many-query and real-time contexts. We develop the RB methodology for a wide range of (coercive and non-coercive) elliptic and parabolic PDEs with several examples drawn from heat transfer, elasticity and fracture, acoustics, and fluid dynamics. We introduce the concept of affine and non-affine parametric dependence, some elements of approximation and algebraic
stability. Finally, we consider application of RB techniques to parameter estimation, optimization, optimal control, and a comparison with other reduced order techniques, like Proper Orthogonal Decomposition.

Lecture notes, slides and reading material is provided during the classes.

Lectures will cover the material in the book : J. Hesthaven, G. Rozza, B. Stamm 'Certified reduced basis methods and a posteriori error bounds for parametrized PDEs', Springer 2015.

Topics/Syllabus
-Introduction to RB methods, offline-online computing, elliptic coercive affine problems
-Sampling, greedy algorithm, POD
-A posteriori error bounds
-Primal-Dual Approximation
-Time dependent problems: POD-greedy sampling
-Non-coercive problems
-Approximation of coercivity and inf-sup parametrized constants
-Geometrical parametrization
-Reference worked problems
-Examples of Applications in CFD
-Tutorials

Schedule
Tuesday,  March 15, 2016: lectures 9:30-13:00, tutorials 14:30-16:15
Wednesday, March 16, 2016: lectures 9:30-13:00, tutorials 14:30-16:00 and 16:30 - 18:00
Thursday, March 17, 2016: lectures 9:30-13:00, tutorials 14:30-16:15

Tutorials are prepared for the course based on FEniCS and Python within the new educational library RBniCS (open Source).