Scientific software

A Julia package for simulating evolutionary individual-based models.

This project aims to develop a high-performance algebraic multigrid (AMG) solver in JAX that supports automatic differentiation and GPU acceleration, addressing critical gaps in existing Python and Julia implementations. The work will enable efficient gradient-based optimization in scientific machine learning applications requiring large-scale linear system solves, with potential to significantly impact the JAX ecosystem for differentiable physics simulations and inverse problems.

Documentation serves multiple purposes and may be useful for various audiences, including your future self, collaborators, users and contributors - should you aim at packaging some of your code into a general-purpose library.

Your future self and others should be able to recreate the minimal environment to run the scripts in your research project. This is best achieved using package managers and virtual environments.

In this post, we’ll explore various methods for structuring a research project folder that includes code, data, publications, and more. Additionally, we’ll look into the specifics of organizing your code folder.

Code testing is essential to identify and fix potential issues, to maintain sanity over the course of the development of the project and quickly identify bugs, and to ensure the reliability and sanity of your experiment overtime.

This website is based on the wowchemy template that relies on Hugo, a fast framework for building static websites. Although Hugo has many advantages over its brother Jekyll, a Hugo website is not as easy to deploy on GitHub Pages, the free service offered by GitHub to host a personal website.