Stochastic Burgers Equation Solutions via Sensitivity-Driven Monte-Carlo Method

Code and data generate figures for a paper on accelerating Monte-Carlo estimation with derivatives of high-level finite element models. The repository, authored by Paul Hauseux from the University of Luxembourg, archives code to solve a 1D stochastic viscous Burgers equation. A Docker image is provided to run the code, which is licensed under LGPL v3.0.

Use Cases

• Benchmarking sensitivity derivative-driven Monte-Carlo methods based on the described numerical technique.
• Reproducing figures from the associated research paper on finite element model acceleration.
• Testing Docker-based computational workflows for scientific computing applications.

Strengths

• Code is accompanied by a Docker image for reproducible execution.
• Includes full instructions in a README.md file as mentioned in the description.
• Licensed under the open-source LGPL v3.0 license.

Limitations

• The dataset's row count and specific file formats are unknown.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Paul.Hauseux Uni.Lu

Collection Method

Code generated for a research paper; archival repository on figshare.