An integration factor method for stochastic and stiff reaction-diffusion systems

Stochastic effects are often present in the biochemical systems involving reactions and diffusions. When the reactions are stiff, existing numerical methods for stochastic reaction diffusion equations require either very small time steps for any explicit schemes or solving large nonlinear systems at each time step for the implicit schemes. Here we present a class of semi-implicit integration factor methods that treat the diffusion term exactly and reaction implicitly for a system of stochastic reaction-diffusion equations. Our linear stability analysis shows the advantage of such methods for both small and large amplitudes of noise. Direct use of the method to solving several linear and nonlinear stochastic reaction-diffusion equations demonstrates good accuracy, efficiency, and stability properties. This new class of methods, which are easy to implement, will have broader applications in solving stochastic reaction-diffusion equations arising from models in biology and physical sciences.

[1]  H. Meinhardt,et al.  Biological pattern formation: fmm basic mechanisms ta complex structures , 1994 .

[2]  E. Weinan,et al.  Effectiveness of implicit methods for stiff stochastic differential equations , 2008 .

[3]  Qing Nie,et al.  Efficient semi-implicit schemes for stiff systems , 2006, J. Comput. Phys..

[4]  Lei Zhang,et al.  Array-representation integration factor method for high-dimensional systems , 2014, J. Comput. Phys..

[5]  I. Famelis,et al.  Numerical Solution of Stochastic Differential Equations with Additive Noise by Runge – Kutta Methods 1 , 2009 .

[6]  Ruth E Baker,et al.  Turing's model for biological pattern formation and the robustness problem , 2012, Interface Focus.

[7]  Qing Nie,et al.  Noise drives sharpening of gene expression boundaries in the zebrafish hindbrain , 2012, Molecular systems biology.

[8]  Jack Xin,et al.  A Critical Quantity for Noise Attenuation in Feedback Systems , 2010, PLoS Comput. Biol..

[9]  Shigeru Kondo,et al.  Reaction-Diffusion Model as a Framework for Understanding Biological Pattern Formation , 2010, Science.

[10]  P. Kloeden,et al.  Overcoming the order barrier in the numerical approximation of stochastic partial differential equations with additive space–time noise , 2009, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[11]  Catherine E. Powell,et al.  An Introduction to Computational Stochastic PDEs , 2014 .

[12]  Liguo Wang,et al.  Numerical Solutions of Stochastic Differential Equations , 2016 .

[13]  D. Wilkinson Stochastic modelling for quantitative description of heterogeneous biological systems , 2009, Nature Reviews Genetics.

[14]  P. Kloeden,et al.  The Numerical Approximation of Stochastic Partial Differential Equations , 2009 .

[15]  P. Kloeden,et al.  Numerical Solutions of Stochastic Differential Equations , 1995 .

[16]  J. Gaines Stochastic Partial Differential Equations: Numerical experiments with S(P)DE's , 1995 .

[17]  Desmond J. Higham,et al.  An Algorithmic Introduction to Numerical Simulation of Stochastic Differential Equations , 2001, SIAM Rev..

[18]  K. Burrage,et al.  Numerical methods for strong solutions of stochastic differential equations: an overview , 2004, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[19]  Qing Nie,et al.  Compact integration factor methods for complex domains and adaptive mesh refinement , 2010, J. Comput. Phys..

[20]  Nazanin Abedini,et al.  Implicit Scheme for Stochastic Partial Differential Equations Driven by Space-Time White Noise , 2014 .

[21]  G. Lord,et al.  A numerical scheme for stochastic PDEs with Gevrey regularity , 2004 .

[22]  Ming Wang,et al.  A robust and efficient method for steady state patterns in reaction-diffusion systems , 2012, J. Comput. Phys..

[23]  Desmond J. Higham,et al.  Mean-Square and Asymptotic Stability of the Stochastic Theta Method , 2000, SIAM J. Numer. Anal..

[24]  A. Abdulle Explicit Methods for Stiff Stochastic Differential Equations , 2012, CSE 2012.

[25]  Xiaojie Wang,et al.  A family of fully implicit Milstein methods for stiff stochastic differential equations with multiplicative noise , 2012 .

[26]  P. Kloeden,et al.  LINEAR-IMPLICIT STRONG SCHEMES FOR ITO-GALKERIN APPROXIMATIONS OF STOCHASTIC PDES , 2001 .

[27]  E. Hausenblas Approximation for Semilinear Stochastic Evolution Equations , 2003 .

[28]  Tianhai Tian,et al.  The composite Euler method for stiff stochastic differential equations , 2001 .

[29]  E. Platen,et al.  Balanced Implicit Methods for Stiff Stochastic Systems , 1998 .

[30]  Brian Drawert,et al.  Spatial Stochastic Dynamics Enable Robust Cell Polarization , 2013, PLoS Comput. Biol..

[31]  Jessica G. Gaines,et al.  Convergence of numerical schemes for the solution of parabolic stochastic partial differential equations , 2001, Math. Comput..

[32]  K. Ritter,et al.  An implicit Euler scheme with non-uniform time discretization for heat equations with multiplicative noise , 2006, math/0604600.

[33]  Qing Nie,et al.  A compact finite difference method for reaction–diffusion problems using compact integration factor methods in high spatial dimensions , 2008, Advances in Difference Equations.