Mean-Square Numerical Methods for Stochastic Differential Equations with Small Noises. - G. N. Milstein; Michael V. Tretyakov (SIAM J. Sci. Comput., 1997)

Numerical algorithms for semilinear parabolic equations with small parameter based on approximation of stochastic equations. - G. N. Milstein; Michael V. Tretyakov (Math. Comput., 2000)

Numerical Methods for Stochastic Systems Preserving Symplectic Structure. - G. N. Milstein; Yu. M. Repin; Michael V. Tretyakov (SIAM J. Numer. Anal., 2002)

Symplectic Integration of Hamiltonian Systems with Additive Noise. - G. N. Milstein; Yu. M. Repin; Michael V. Tretyakov (SIAM J. Numer. Anal., 2002)

Numerical Integration of Stochastic Differential Equations with Nonglobally Lipschitz Coefficients. - G. N. Milstein; Michael V. Tretyakov (SIAM J. Numer. Anal., 2005)

Numerical Algorithms for Forward-Backward Stochastic Differential Equations. - G. N. Milstein; Michael V. Tretyakov (SIAM J. Sci. Comput., 2006)

Solving parabolic stochastic partial differential equations via averaging over characteristics. - G. N. Milstein; Michael V. Tretyakov (Math. Comput., 2009)

Practical Variance Reduction via Regression for Simulating Diffusions. - G. N. Milstein; Michael V. Tretyakov (SIAM J. Numer. Anal., 2009)

Convergence of Numerical Time-Averaging and Stationary Measures via Poisson Equations. - Jonathan C. Mattingly; Andrew M. Stuart; Michael V. Tretyakov (SIAM J. Numer. Anal., 2010)

A Multistage Wiener Chaos Expansion Method for Stochastic Advection-Diffusion-Reaction Equations. - Zhongqiang Zhang; Boris Rozovskii; Michael V. Tretyakov; George E. Karniadakis (SIAM J. Sci. Comput., 2012)

A Fundamental Mean-Square Convergence Theorem for SDEs with Locally Lipschitz Coefficients and Its Applications. - Michael V. Tretyakov; Zhongqiang Zhang (SIAM J. Numer. Anal., 2013)

A Recursive Sparse Grid Collocation Method for Differential Equations with White Noise. - Zhongqiang Zhang; Michael V. Tretyakov; Boris Rozovskii; George E. Karniadakis (SIAM J. Sci. Comput., 2014)

Wiener Chaos Versus Stochastic Collocation Methods for Linear Advection-Diffusion-Reaction Equations with Multiplicative White Noise. - Zhongqiang Zhang; Michael V. Tretyakov; Boris Rozovskii; George E. Karniadakis (SIAM J. Numer. Anal., 2015)

Layer methods for stochastic Navier-Stokes equations using simplest characteristics. - G. N. Milstein; Michael V. Tretyakov (J. Comput. Appl. Math., 2016)

Stochastic Resin Transfer Molding Process. - M. Park; Michael V. Tretyakov (SIAM/ASA J. Uncertain. Quantification, 2017)

Random walk algorithm for the Dirichlet problem for parabolic integro-differential equation. - George Deligiannidis; S. Maurer; Michael V. Tretyakov (CoRR, 2020)

Simplest random walk for approximating Robin boundary value problems and ergodic limits of reflected diffusions. - Benedict J. Leimkuhler; Akash Sharma; Michael V. Tretyakov (CoRR, 2020)

Option Valuation through Deep Learning of Transition Probability Density. - Haozhe Su; Michael V. Tretyakov; David P. Newton (CoRR, 2021)

Consensus based optimization via jump-diffusion stochastic differential equations. - Dante Kalise; Akash Sharma; Michael V. Tretyakov (CoRR, 2022)

Neural variance reduction for stochastic differential equations. - P. D. Hinds; Michael V. Tretyakov (CoRR, 2022)

Sampling and estimation on manifolds using the Langevin diffusion. - Karthik Bharath; Alexander Lewis; Akash Sharma; Michael V. Tretyakov (CoRR, 2023)

Numerical integrators for confined Langevin dynamics. - Ben Leimkuhler; Akash Sharma; Michael V. Tretyakov (CoRR, 2024)

Well-posedness and approximation of reflected McKean-Vlasov SDEs with applications. - P. D. Hinds; Akash Sharma; Michael V. Tretyakov (CoRR, 2024)

Deep Learning of Transition Probability Densities for Stochastic Asset Models with Applications in Option Pricing. - Haozhe Su; Michael V. Tretyakov; David P. Newton (Manag. Sci., 2025)

Sampling from Mixture Distributions Based on Regime-Switching Diffusions. - Michael V. Tretyakov (SIAM J. Sci. Comput., 2025)