The Annals of Applied Probability

Approximations of stochastic partial differential equations

Giulia Di Nunno and Tusheng Zhang

Full-text: Open access

Abstract

In this paper, we show that solutions of stochastic partial differential equations driven by Brownian motion can be approximated by stochastic partial differential equations forced by pure jump noise/random kicks. Applications to stochastic Burgers equations are discussed.

Article information

Source
Ann. Appl. Probab., Volume 26, Number 3 (2016), 1443-1466.

Dates
Received: February 2014
Revised: April 2015
First available in Project Euclid: 14 June 2016

Permanent link to this document
https://projecteuclid.org/euclid.aoap/1465905008

Digital Object Identifier
doi:10.1214/15-AAP1122

Mathematical Reviews number (MathSciNet)
MR3513595

Zentralblatt MATH identifier
1345.60053

Subjects
Primary: 60H15: Stochastic partial differential equations [See also 35R60]
Secondary: 93E20: Optimal stochastic control 35R60: Partial differential equations with randomness, stochastic partial differential equations [See also 60H15]

Keywords
Stochastic partial differential equations approximations jump noise tightness weak convergence stochastic Burgers equations

Citation

Di Nunno, Giulia; Zhang, Tusheng. Approximations of stochastic partial differential equations. Ann. Appl. Probab. 26 (2016), no. 3, 1443--1466. doi:10.1214/15-AAP1122. https://projecteuclid.org/euclid.aoap/1465905008


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