Scientific Computing I

What it is about

You will learn the steps of the scientific computing pipeline in scientific data processing, with a focus on model development and discretization. In the end you will be able to derive and classify simple mathematical models and apply suitable discretization and time integration methods, and assess their accuracy and suitability.

What you will be able to do

• Know the steps of the scientific computing pipeline
• Classify mathematical models and derive simple models
• Analyze the behavior of models (e.g., critical points, asymptotics)
• Apply common discretization methods for PDEs and ODEs
• Use explicit and implicit time integrators
• Assess suitability, stability, consistency, accuracy and convergence of numerical methods
• Discuss typical sources of error and limitations of models and discretizations
• Understand the impact of further pipeline steps (e.g., parallelization, visualization) on model choice
• Develop awareness of the societal and economic relevance of simulation results

What the module consists of

• VorlesungDelivery of theoretical content and concepts through lectures and presentations
• Übungen/TutorialsSolving concrete problems, partly in teams, and discussion of selected examples

Teaching method

• Vorträge/PräsentationenIntroduction and explanation of the contents
• Selbststudium (Literaturarbeit)Deepening of individual topics through independent study of the recommended literature
• Tutorien mit AufgabenApplication of what has been learned to concrete problems and training in problem solving