Scientific Computation and Applied & Industrial Mathematics: Simone Brugiapaglia

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.