Monte-Carlo algorithms for a forward Feynman–Kac-type representation for semilinear nonconservative partial differential equations

Abstract The paper is devoted to the construction of a probabilistic particle algorithm. This is related to a nonlinear forward Feynman–Kac-type equation, which represents the solution of a nonconservative semilinear parabolic partial differential equation (PDE). Illustrations of the efficiency of the algorithm are provided by numerical experiments.

[1]  Mireille Bossy,et al.  COMPARISON OF A STOCHASTIC PARTICLE METHOD AND A FINITE VOLUME DETERMINISTIC METHOD APPLIED TO BURGERS EQUATION , 1997, Monte Carlo Methods Appl..

[2]  E. Gobet,et al.  A regression-based Monte Carlo method to solve backward stochastic differential equations , 2005, math/0508491.

[3]  D. Talay,et al.  Convergence Rate for the Approximation of the Limit Law of Weakly Interacting Particles 2: Application to the Burgers Equation , 1996 .

[4]  Francesco Russo,et al.  Particle system algorithm and chaos propagation related to non-conservative McKean type stochastic differential equations , 2016, Stochastics and Partial Differential Equations: Analysis and Computations.

[5]  Xiaolu Tan,et al.  A numerical algorithm for a class of BSDEs via branching process , 2013 .

[6]  Marco Pavone,et al.  Stochastic Optimal Control , 2015 .

[7]  Nadia Oudjane,et al.  Branching diffusion representation of semilinear PDEs and Monte Carlo approximation , 2016, Annales de l'Institut Henri Poincaré, Probabilités et Statistiques.

[8]  Bernard W. Silverman,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[9]  Etienne Pardoux,et al.  Stochastic Differential Equations, Backward SDEs, Partial Differential Equations , 2014 .

[10]  Andrew W. Moore,et al.  Nonparametric Density Estimation: Toward Computational Tractability , 2003, SDM.

[11]  H. Soner,et al.  Second‐order backward stochastic differential equations and fully nonlinear parabolic PDEs , 2005, math/0509295.

[12]  S. Peng,et al.  Adapted solution of a backward stochastic differential equation , 1990 .

[13]  Alain Bensoussan,et al.  Stochastic Production Planning with Production Constraints , 1980 .

[14]  T. Faniran Numerical Solution of Stochastic Differential Equations , 2015 .

[15]  Francesco Russo,et al.  Probabilistic representation of a class of non conservative nonlinear Partial Differential Equations , 2015, 1504.03882.

[16]  Pierre Henry-Labordere,et al.  Counterparty Risk Valuation: A Marked Branching Diffusion Approach , 2012, 1203.2369.

[17]  P. J. Green,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[18]  Francesco Russo,et al.  Probabilistic and deterministic algorithms for space multidimensional irregular porous media equation , 2013 .

[19]  Francesco Russo,et al.  A probabilistic algorithm approximating solutions of a singular PDE of porous media type , 2010, Monte Carlo Methods Appl..

[20]  Xiaolu Tan,et al.  A Numerical Algorithm for a Class of BSDE Via Branching Process , 2013 .

[21]  S. Shreve,et al.  Stochastic differential equations , 1955, Mathematical Proceedings of the Cambridge Philosophical Society.

[22]  Benjamin Jourdain,et al.  Propagation of chaos and fluctuations for a moderate model with smooth initial data , 1998 .

[23]  É. Pardoux Backward Stochastic Differential Equations and Viscosity Solutions of Systems of Semilinear Parabolic and Elliptic PDEs of Second Order , 1998 .

[24]  B. Bouchard,et al.  Discrete-time approximation and Monte-Carlo simulation of backward stochastic differential equations , 2004 .

[25]  François Delarue,et al.  An interpolated stochastic algorithm for quasi-linear PDEs , 2008, Math. Comput..

[26]  Francesco Russo,et al.  Forward Feynman-Kac type representation for semilinear non-conservative partial differential equations , 2016, Stochastics.