Numerical Approximation of Topological Derivatives and Applications
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore a comprehensive lecture on numerical approximation of topological derivatives and their applications in design optimization and mathematical imaging. Delve into the concept of topological derivatives and their role in representing sensitivity to small material inclusions in design-dependent cost functions. Examine closed-form formulas for PDE-constrained topology optimization problems and learn about numerical approximation techniques for non-standard inclusion shapes and quasilinear PDEs. Discover practical applications in multi-material design optimization of electric machines and corner detection in mathematical imaging. Gain insights from this talk, which was part of the Workshop on "New perspective on Shape and Topology Optimization" held at the Erwin Schrödinger International Institute for Mathematics and Physics.