The course aims to offer an enlarged vision on the various aspects - both from the theoretical and implementation viewpoints - that characterize the modern use of Scientific Computing, along with its application to problems arising in physics, biology and engineering.
Expected learning outcomes
Upon completing the course, the students will be able to apply adequate discretization techniques to handle partial differential equation problems of elliptic, parabolic and hyperbolic type. They will also be able to quantify the accuracy of the chosen method and to produce an adequate implementation in Matlab.
Lesson period: Second semester
(In case of multiple editions, please check the period, as it may vary)
Propedeuticità consigliate Corso di base di Calcolo Numerico, Programmazione Matlab, Analisi Matematica
Materiale di riferimento Appunti del corso, materiale fornito dalla docente Introduction to partial differential equation problems and their importance in the applications. Non-dimensionalization and scaling procedures. Discretization of ordinary derivative equations: multistep and Runge Kutta methods. Analysis and Matlab implementation. Partial derivative equations: theoretical properties and finite difference discretization in 1D and nD for elliptic and parabolic equations. Convection-diffusion-reaction problems with transport / dominant reaction. Introduction to inverse problems. Introduction to
Prerequisites for admission
Fundamentals of Numerical Analysis, Matlab Programming, Calculus
lectures and lab sessions
notes of the course, study material provided during the course
Assessment methods and Criteria
The exam is composed of 4 projects to be delivered during the term. The projects aim to verify the competences and include theoretical and coding questions