How can a robot vacuum determine an optimal path through a room full of obstacles? How can a large swarm of autonomous drones or vehicles be controlled to efficiently accomplish a task? How to identify the dominant features of an image or data set?
Partial differential equations (PDEs) arise in countless mathematical models in physics, engineering, statistics, image processing, economics and many other fields. In this course, we will begin by exploring important fundamentals of PDEs and the calculus of variations. Since virtually all real-life problems are too complex to be solved analytically, we will study and implement numerical methods such as finite differences and finite elements, which will finally allow us to tackle challenging problems from applications of students' interest.
This elective course is designed to foster development of analytical, computational and professional skills. Moreover, it covers some of the latest advances in the field, hence equipping participants with the indispensable skill set for a future career in science or R&D.
Link zu den Modulbeschreibungen im Service
AR308, AR214