Scientific Computing
A.Y. 2018/2019
Learning objectives
The course provides students with an enlarged vision on the various aspects - both from the theoretical and implementation viewpoints - that characterize the modern use of Scientific Computing and its application to problems from physics, biology and engineering.
Expected learning outcomes
Upon completing the course, student 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
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Single session
Responsible
Lesson period
Second semester
Course syllabus
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, parabolic, hyperbolic equations. Convection-diffusion-reaction problems with transport / dominant reaction. Matlab implementation. Bio-informatics problems, applications to medicine
MAT/08 - NUMERICAL ANALYSIS - University credits: 6
Laboratories: 24 hours
Lessons: 36 hours
Lessons: 36 hours
Professor:
Causin Paola
Professor(s)