Session Time & Room : 3D (Aug.23, 15:30-17:10) @E811
Type : Contributed Talk
Abstract :
We introduce a novel mesh-free method for computing the shape derivative in PDE-constrained shape optimization problems. Our approach is based on a probabilistic deep solver, which can be shown to converge for a wide class of seminilinear PDEs, and a suitable representation of the shape gradient. In contrast to finite element, volume and difference methods, our approach does not require a discretization of the domain’s interior. We also present examples for performance illustration.