A Monte Carlo method for solving the one-dimensional telegraph equations with boundary conditions

A Monte Carlo algorithm is derived to solve the one-dimensional telegraph equations in a bounded domain subject to resistive and non-resistive boundary conditions. The proposed numerical scheme is more efficient than the classical Kac's theory because it does not require the discretization of time. The algorithm has been validated by comparing the results obtained with theory and the Finite-difference time domain (FDTD) method for a typical two-wire transmission line terminated at both ends with general boundary conditions. We have also tested transmission line heterogeneities to account for wave propagation in multiple media. The algorithm is inherently parallel, since it is based on Monte Carlo simulations, and does not suffer from the numerical dispersion and dissipation issues that arise in finite difference-based numerical schemes on a lossy medium. This allowed us to develop an efficient numerical method, capable of outperforming the classical FDTD method for large scale problems and high frequency signals.

[1]  R. Vilela Mendes,et al.  Poisson-Vlasov in a strong magnetic field: A stochastic solution approach , 2009, 0904.2214.

[2]  Ioannis Karatzas,et al.  Brownian Motion and Stochastic Calculus , 1987 .

[3]  M. Kac A stochastic model related to the telegrapher's equation , 1974 .

[4]  E. Orsingher Probability law, flow function, maximum distribution of wave-governed random motions and their connections with Kirchoff's laws , 1990 .

[5]  R. Janaswamy,et al.  On random time and on the relation between wave and telegraph equations , 2013, IEEE Transactions on Antennas and Propagation.

[6]  Tim B. Swartz,et al.  Approximating Integrals Via Monte Carlo and Deterministic Methods , 2000 .

[7]  Juan A. Acebrón,et al.  A new parallel solver suited for arbitrary semilinear parabolic partial differential equations based on generalized random trees , 2011, J. Comput. Phys..

[8]  David B. Bogy,et al.  Novel solutions of the Helmholtz equation and their application to diffraction , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[9]  R. Mattheij,et al.  Partial Differential Equations: Modeling, Analysis, Computation (Siam Monographs on Mathematical Modeling and Computation) (Saim Models on Mathematical Modeling and Computation) , 2005 .

[10]  Piero Lanucara,et al.  Probabilistically induced domain decomposition methods for elliptic boundary-value problems , 2005 .

[11]  R. Garra,et al.  Random flights governed by Klein-Gordon-type partial differential equations , 2013, 1311.0128.

[12]  S. Goldstein ON DIFFUSION BY DISCONTINUOUS MOVEMENTS, AND ON THE TELEGRAPH EQUATION , 1951 .

[13]  Michael T. Heath,et al.  Scientific Computing: An Introductory Survey , 1996 .

[14]  R.M.M. Mattheij,et al.  Partial Differential Equations: Modeling, Analysis, Computation (Siam Monographs on Mathematical Modeling and Computation) (Saim Models on Mathematical Modeling and Computation) , 2005 .

[15]  R. Vilela Mendes,et al.  Stochastic solutions of some nonlinear partial differential equations , 2009 .

[16]  David B. Davidson,et al.  Computational Electromagnetics for RF and Microwave Engineering , 2005 .

[17]  Alessandro De Gregorio,et al.  Flying randomly in Rd with Dirichlet displacements , 2012 .

[18]  Chun-Hao Teng,et al.  The instability of the Yee Scheme for the “Magic Time Step” , 2001 .

[19]  Guilin Sun,et al.  Numerical dispersion and numerical loss in explicit finite-difference time-domain methods in lossy media , 2005, IEEE Transactions on Antennas and Propagation.

[20]  Juan A. Acebrón,et al.  Supercomputing applications to the numerical modeling of industrial and applied mathematics problems , 2006, The Journal of Supercomputing.

[21]  M. Kac Random Walk and the Theory of Brownian Motion , 1947 .

[22]  M. Sadiku Monte Carlo Methods for Electromagnetics , 2009 .

[23]  Sean McKee,et al.  Monte Carlo Methods for Applied Scientists , 2005 .

[24]  M. Sadiku Numerical Techniques in Electromagnetics , 2000 .

[25]  Y. L. Le Coz,et al.  Performance of random-walk capacitance extractors for IC interconnects: A numerical study , 1998 .

[26]  Juan A. Acebrón,et al.  Highly efficient numerical algorithm based on random trees for accelerating parallel Vlasov-Poisson simulations , 2013, J. Comput. Phys..

[27]  Siddhartha Gadgil,et al.  A chain complex and Quadrilaterals for normal surfaces , 2008 .