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[01104] Generation of $hp$-FEM Massive Databases for Deep Learning Inversion

  • Session Time & Room : 2D (Aug.22, 15:30-17:10) @E506
  • Type : Contributed Talk
  • Abstract : Deep Neural Networks are employed in many geophysical applications to characterize the Earth’s subsurface. However, they often need to solve hundreds of thousands of complex and expensive forward problems to produce the training dataset. This work presents a robust approach to producing massive databases at a reduced computational cost. In particular, we build a single $hp$-adapted mesh that accurately solves many FEM problems for any combination of parameters within a given range.
  • Classification : 65N30, Finite Element Method, Deep Neural Networks, Goal-Oriented Adaptivity
  • Format : Talk at Waseda University
  • Author(s) :
    • Julen Alvarez-Aramberri (University of the Basque Country (UPV/EHU))
    • Vincent Darrigrand (CNRS-IRIT, Toulouse)
    • Felipe Vinicio Caro (Basque Center for Applied Mathematics (BCAM), University of the Basque Country (UPV/EHU))
    • David Pardo (University of the Basque Country (UPV-EHU), Basque Center for Applied Mathematics (BCAM), Ikerbasque)