Registered Data

[00476] Hierarchical Sampling Techniques and Goal-Oriented Adaptive Finite Element for Elliptic PDE with Lognormal Coefficients

  • Session Time & Room : 5B (Aug.25, 10:40-12:20) @E505
  • Type : Contributed Talk
  • Abstract : We propose our Adaptive Multilevel Monte Carlo (AMLMC) method to solve an elliptic partial differential equation with lognormal random input data where the PDE model has geometry-induced singularities. This work combines (MLMC) and the dual-weighted-residual goal-oriented adaptive finite element. Specifically, for a given input coefficient realization and an accuracy level, the (AMLMC) constructs its approximate sample as the ones using the first mesh in the sequence of pre-generated, non-uniform meshes satisfying the sample-dependent bias constraint.
  • Classification : 65C05, 65N50, 65N22, 35R60
  • Format : Talk at Waseda University
  • Author(s) :
    • Joakim Beck (King Abdullah University of Science and Technology)
    • Yang Liu (King Abdullah University of Science and Technology)
    • Erik von Schwerin (King Abdullah University of Science and Technology)
    • Raul Tempone (King Abdullah University of Science and Technology)