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[01196] Deep Solvers in Shape Optimization

  • 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.
  • Classification : 68T07, 65N99
  • Format : Talk at Waseda University
  • Author(s) :
    • Maximilian Würschmidt (Trier University)
    • Frank Seifried (Trier University)
    • Luka Schlegel (Trier University)
    • Volker Schulz (Trier University)