For gradient based shape optimization the application of second order derivatives could greatly improve performance. Many possible ways to approximate a Hessian have been investigated in the past. In this talk we will discuss the Sobolev gradient method for incorporating second order derivative information. This talk will give an overview of the mathematical background and show a sequential quadratic programming method for this case. Furthermore, the implementation of this method into the existing shape optimization capabilities of the SU2 multiphysics and design software will be discussed
How to join
The talk is held online via Jitsi. You can join with the link https://jitsi.uni-kl.de/SciCompSeminar_04. Please follow the rules below:
- Use a chrome based browser (One member with a different browser can crash the whole meeting).
- Mute your microphone and disable your camera.
- If you have a question, raise your hand.
More information is available at https://www.rhrk.uni-kl.de/dienstleistungen/netz-telefonie/konferenzdienste/jitsi/.
Referent: T. Dick, AG Scientific Computing, TU Kaiserslautern
Zeit: 11:30 Uhr