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[01390] Neural Operator for Multidisciplinary Engineering Design

  • Session Time & Room : 4E (Aug.24, 17:40-19:20) @G501
  • Type : Contributed Talk
  • Abstract : Deep learning surrogate models have shown promise in solving PDEs, which enable many-query computations in science and engineering. In this talk, I will first introduce a geometry-aware Fourier neural operator (Geo-FNO) to solve PDEs on arbitrary geometries, inspired by adaptive mesh motion and spectral methods. Furthermore, we study the cost-accuracy trade-off of different deep learning-based surrogate models, following traditional numerical error analysis. Finally, we demonstrate our approach on challenging engineering design problems.
  • Classification : 35C99, 65M99, 65Z05, 68T07
  • Format : Talk at Waseda University
  • Author(s) :
    • Daniel Zhengyu Huang (Caltech)
    • Andrew M. Stuart (Caltech)
    • Elizabeth Qian ( Georgia Tech)
    • Maarten de Hoop (Rice University)