A Fast Explicit Fetd Method Based on Compressed Sensing

Abstract

Abstract-Linear equations must be solved at each time step as the explicit finite element time-domain (FETD) method is used to solve time dependent Maxwell curl equations, which leads to a huge amount of computational cost in a long period time simulation. A new scheme to accelerate the iteration solution for matrix equation is proposed based on compressed sensing (CS), in which a low rank measurement matrix is established by randomly extracting rows from mass matrix. Meanwhile, to reduce the number of measurements required, a sparse transform is constructed with the help of prior knowledge offered by the solution results of previous time steps. Numerical results of homogeneous cavity and inhomogeneous cavity are discussed to validate the effectiveness and accuracy of the proposed approach.

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“…B2 promotes intestinal protease expression and activity that likely promotes digestion. [113] In this context, B2 may be required for the absorption of many nutrients, and its absence may induce a starvation state. TORC1 signaling is required for the growth-promoting effects of B2, and B2 addition promotes TOR activation in seam cells.…”

Are you using the right tools in computational electromagnetics?

“…Recently, a couple of interesting explicit FETD implementations were demonstrated using clever matrix manipulations. 106,107 These explicit FETD tools are still not widely used because of the complicated matrix manipulation and conditioning required to run these methods. These tools demand more from the users in terms of understanding the underlying mathematical manipulations.…”