Mathematics

The course "Numerical Solution of PDEs using the Finite Element Method" offers a focused exploration of solving Partial Differential Equations (PDEs) using the Finite Element Method (FEM), employing the deal.II software library. Key components of the course include an introduction to PDEs, basics of numerical methods and FEM analysis, practical training using deal.II, and hands-on projects. The course will also cover High-Performance Computing (HPC) techniques for parallelizing, optimizing, and load balancing FEM simulations for real-world applications.

Syllabus 2023-2024

• Basics on Scientific Computing
• Vector spaces, vector norms, matrices, and matrix norms
• Basic linear algebra: direct solution of linear systems
• Not so basic linear algebra: iterative solution of linear systems
• Polynomial interpolation
• Interpolatory Quadrature rules
• L2 projection / Least square approximation
• Introduction to Finite Difference Methods
• Introduction to Finite Element Methods

Python laboratories

Ordinary differential equations (ODEs) describe many physical, biological and chemical phenomena. It is, thus, important to find approximations of ODEs which are highly accurate and, in order to obtain it within reasonable computational times, high order accurate time integration methods are very often chosen to proceed in time. In this course, we will revise ODEs and the theoretical results that guarantee their existence and uniqueness [1, 2].

Students will acquire a comprehensive understanding of advanced programming concepts, specifically in C++ and Python. They will become familiar with object-oriented and generic programming paradigms, as well as proficient in utilizing common data structures, algorithms, and relevant libraries and frameworks for scientific computing. Furthermore, students will be introduced to fundamental software development tools in a Linux environment, encompassing essential aspects like software documentation, version control, testing, and project management.