Automatic Differentiation for large scale flow control with application to non-Newtonian flows

• Antragstellerinnen / Antragsteller: Professor Dr. Vincent Heuveline; Professorin Dr. Andrea Walther
• Fachliche Zuordnung: Mathematik
• Förderung: Förderung von 2006 bis 2010
• Projektkennung: Deutsche Forschungsgemeinschaft (DFG) - Projektnummer 25596922

This project focuses on the development, analysis, and implementation of efficient numerical optimization algorithms using Automatic Differentiation techniques in the context of flow control problems including highly nonlinear PDE constraints. The developed PDE-constrained optimization algorithms will be applied to the stabilization of flows involving non-Newtonian fluids as for example blood flows and sedimentation problems. The considered models include e.g. memory effects of the fluid which lead to complex and highly nonlinear state equations. These problems have in common that the determination of the linearized state equation needed for the adjoints or for the sensitivities would be an extremely tedious task if possible at all. Our goal is to apply in a systematic way techniques of automatic differentiation in that context. A special emphasis is given to goal oriented adaptivity, optimal experimental design toward model calibration, and parallel processing in that framework.

• DFG-Verfahren: Schwerpunktprogramme
• Teilprojekt zu: SPP 1253: Optimierung mit partiellen Differentialgleichungen