Publications：

• Neufeld, A., Schmocker, P., and Wu, S., Full error analysis of the random deep splitting method for nonlinear parabolic PDEs and PIDEs, Commun Nonlinear Sci Numer Simulat, 143 (2025): 108556.

• Neufeld, A., and Wu, S., Multilevel Picard approximation algorithm for semilinear partial integro-differential equations and its complexity analysis, Stoch PDE: Anal Comp, (2025): doi.org/10.1007/s40072-025-00355-2.

• Neufeld, A., Nguyen, T. A., and Wu, S., Deep ReLU neural networks overcome the curse of dimensionality when approximating semilinear partial integro-differential equations, Analysis and Applications, (2025): doi.org/10.1142/S021953052550006X.

• Neufeld, A., and Wu, S., Multilevel Picard approximation algorithm for semilinear partial differential equations with gradient-dependent nonlinearity, to appear in Journal of Numerical Mathematics, (2025).

• Neufeld, A., Nguyen, T.A., and Wu, S., Multilevel Picard approximations overcome the curse of dimensionality in the numerical approximation of general semilinear PDEs with gradient-dependent nonlinearities, to appear in Journal of Complexity, (2025).

• Gyöngy , I. and Wu, S., Itô’s formula for jump processes in Lp-spaces, Stochastic processes and their applications, 131 (2021): 523-552.

• Gyöngy , I., Wu, S., On Lp-solvability of stochastic integro-differential equations, Stoch PDE: Anal Comp, 9, no.2 (2021): 295-342.

• De Léon-Contreras, M., Gyöngy, I. and Wu, S. On solvability of integro-differential equations. Potential Anal, 55, no.3 (2021): 443-475.

• Gyöngy , I. and Wu, S., On Itô’s formula for jump processes, Queueing Systems, 98, no.3 (2021): 247-273.

Preprints:

• Neufeld, A., Schmocker, P., and Wu, S., Approximation error analysis of the deep splitting algorithm for semilinear PDEs with gradient-dependent nonlinearities, in progress.