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[00875] Deep learning based reduced ensemble Kalman inversion for microscopic parameter estimation

  • Session Time & Room : 2C (Aug.22, 13:20-15:00) @G710
  • Type : Contributed Talk
  • Abstract : In the scope of nonlinear multiscale problems, estimating the macroscopic distribution of the microscopic geometrical parameters given macroscopic measurements is of interest. In general, inverse estimation is challenging due to the need of derivatives of the complex forward model and the high cost of the forward solver. We introduce derivative-free ensemble Kalman inversion and deep-learning based model reduction to tackle the aforementioned challenges, and assess the performance of the proposed method on a hyper-elastic problem.
  • Classification : 35R30, 65N21, 74G75, 65N75, 62F86
  • Format : Talk at Waseda University
  • Author(s) :
    • Yankun Hong (Eindhoven University of Technology)
    • Harshit Bansal (Eindhoven University of Technology)
    • Karen Veroy (Eindhoven University of Technology)