Scalable Preconditioners for Structure Preserving Discretizations of Maxwell Equations in First Order Form
Author(s) -
Edward Geoffrey Phillips,
John N. Shadid,
Eric C. Cyr
Publication year - 2018
Publication title -
siam journal on scientific computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.674
H-Index - 147
eISSN - 1095-7197
pISSN - 1064-8275
DOI - 10.1137/17m1135827
Subject(s) - preconditioner , multigrid method , maxwell's equations , mathematics , dissipative system , stability (learning theory) , scalability , boundary value problem , linear system , computer science , partial differential equation , mathematical analysis , physics , database , quantum mechanics , machine learning
Multiple physical time-scales can arise in electromagnetic simulations when dissipative effects are introduced through boundary conditions, when currents follow external time-scales, and when material parameters vary spatially. In such scenarios, the time-scales of interest may be much slower than the fastest time-scales supported by the Maxwell equations, therefore making implicit time integration an efficient approach. The use of implicit temporal discretizations results in linear systems in which fast time-scales, which severely constrain the stability of an explicit method, can manifest as so-called stiff modes. This study proposes a new block preconditioner for structure preserving (also termed physics compatible) discretizations of the Maxwell equations in first order form. The intent of the preconditioner is to enable the efficient solution of multiple-time-scale Maxwell type systems. An additional benefit of the developed preconditioner is that it requires only a traditional multigrid method for i...
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom