Scientific Computation and Applied & Industrial Mathematics: Simone Brugiapaglia

  • Date: 11/29/2016
  • Time: 12:30
Simone Brugiapaglia, Simon Fraser University

University of British Columbia


The benefits of Compressed Sensing for the Numerical Approximation of PDEs


We present the CORSING (COmpRessed SolvING) method for the numerical approximation of PDEs. Establishing an analogy between the bilinear form associated with the weak formulation of a PDE and the signal acquisition process, CORSING combines the classical Petrov-Galerkin method with compressed sensing. This allows for a dramatic dimensionality reduction of the PDE discretization and for an efficient recovery of sparse solutions.
Considering the advection-diffusion-reaction equation of fluid dynamics as a case study, we discuss some numerical examples in MATLAB and analyze the method from a theoretical perspective. In particular, we provide recovery error estimates in expectation and in probability, employing wavelets and Fourier basis functions.

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Location: ESB 4133 (PIMS Lounge)