Research
Photo taken @ Edinburgh by LeeYoung Photography
Publications：
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., 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, arXiv:2311.11579 (2023).
Neufeld, A., and Wu, S., Multilevel Picard approximation algorithm for semilinear partial differential equations with gradient-dependent nonlinearity, arXiv:2310.12545v2 (2023).
Neufeld, A., Nguyen, T. A., and Wu, S., Deep ReLU neural networks overcome the curse of dimensionality when approximating semilinear partial integro-differential equations, arXiv:2310.15581v2 (2023), submitted.
Neufeld, A., Schmocker, P., and Wu, S., Deep splitting schemes for semilinear parabolic partial integro-differential equations, arXiv:2405.05192 (2023), submitted.
Neufeld, A., and Wu, S., Multilevel Picard approximation algorithm for semilinear partial integro-differential equations and its complexity analysis, arXiv:2205.09639 (2022), submitted.
Neufeld, A., and Wu, S., Approximation error analysis of the deep splitting algorithm for semilinear PDEs with gradient-dependent nonlinearities, in progress.