Solving the stochastic Burgers equation with a sensitivity derivative-driven Monte Carlo method

Code to solve a 1D stochastic viscous Burgers equation using a sensitivity derivative-driven Monte Carlo method. A Docker image is provided that can run all of the included code. This code generates the figures shown in the paper: Accelerating Monte Carlo estimation with derivatives of high-level finite element models, P. Hauseux, J.S. Hale, S.P.A. Bordas. 1 May 2017. 318, pp. 917-936. Computer Methods in Applied Mechanics and Engineering.http://dx.doi.org/10.1016/j.cma.2017.01.041http://hdl.handle.net/10993/28618This figshare repository is for archival purposes. It is easiest to use the code and image directly from the Bitbucket and Dockerhub repositories shown in the References section below. Full instructions are given in the README.md file inside the code archive file. The code is licensed under the LGPL v3.0. The Docker image contains binaries for a variety of open source software. All software binaries in the container were compiled from unmodified sources from the project originators.

本代码基于灵敏度导数驱动的蒙特卡洛方法（Monte Carlo method），求解一维随机粘性伯格斯方程（1D stochastic viscous Burgers equation）。本仓库提供可运行全部内置代码的Docker镜像（Docker image），可复现下述论文中的配图：《基于高阶有限元模型导数加速蒙特卡洛估计》（Accelerating Monte Carlo estimation with derivatives of high-level finite element models），作者为P. Hauseux、J.S. Hale及S.P.A. Bordas，发表于2017年5月1日，刊载于《应用力学与工程计算机方法（Computer Methods in Applied Mechanics and Engineering）》第318卷，第917至936页。相关链接：http://dx.doi.org/10.1016/j.cma.2017.01.041、http://hdl.handle.net/10993/28618。本figshare仓库仅用于存档，建议直接通过下文参考文献部分提及的Bitbucket及Dockerhub仓库获取代码与镜像以获得最佳使用体验。代码压缩包内的README.md文件已提供完整操作指南。本代码采用LGPL v3.0开源许可协议。该Docker镜像包含多款开源软件的二进制文件，容器内所有软件二进制文件均由项目原始发起者提供的未修改源码编译而来。