# automan: a simple, Python-based, automation framework for numerical computing

arXiv: https://arxiv.org/abs/1712.04786 · HTML: https://mlsanity.com/paper/1712.04786/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Dec 2017.

## Built on by (most cited first, 2 of 2)

- [SPINN: Sparse, Physics-based, and partially Interpretable Neural Networks for PDEs](https://mlsanity.com/paper/2102.13037/) (2021)
- [Anisotropic, Sparse and Interpretable Physics-Informed Neural Networks for PDEs](https://mlsanity.com/paper/2207.00377/) (2022)

## Abstract (arXiv)

We present an easy-to-use, Python-based framework that allows a researcher to automate their computational simulations. In particular the framework facilitates assembling several long-running computations and producing various plots from the data produced by these computations. The framework makes it possible to reproduce every figure made for a publication with a single command. It also allows one to distribute the computations across a network of computers. The framework has been used to write research papers in numerical computing. This paper discusses the design of the framework, and the benefits of using it. The ideas presented are general and should help researchers organize their computations for better reproducibility.