Scientific computing

Algebraic multigrid solvers in JAX.

A minimal JAX library for graph-based connectivity analysis at scale.

Funding for the CORDS course, coordinated by Mauro Werder.

Workshop material for modelling and data analysis in biodiversity and earth sciences.

Funding for a three-day Julia workshop for biodiversity research.

Course material on reproducible research, data pipelines, and scientific computing.

Funding for a Julia workshop on modelling and data analysis in biodiversity and earth sciences.

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.