Scientific computing | Victor Boussange

AMJAX

Thu, 01 Jan 2026 00:00:00 +0000

jaxscape

Thu, 01 Jan 2026 00:00:00 +0000

A course on reproducible research, data pipelines, and scientific computing

Tue, 11 Jun 2024 00:00:00 +0000

Practical introduction to Julia for biodiversity research

Wed, 01 Mar 2023 00:00:00 +0000

yDiv workshop funding

Thu, 01 Aug 2024 00:00:00 +0000

Course On Reproducible Research, Data Pipelines, and Scientific Computing

Tue, 11 Jun 2024 00:00:00 +0000

WSL Biodiversity Center Innovative Workshop Grant

Wed, 01 Mar 2023 00:00:00 +0000

Chaotic Slow Slip Events in New Zealand from Two Coupled Slip Patches: A Proof of Concept

Fri, 09 Jan 2026 00:00:00 +0000

A Unified Framework for Prioritizing Habitat and Connectivity Conservation through Analytical Sensitivity

Mon, 05 Jan 2026 00:00:00 +0000

Developing an algebraic multigrid solver in JAX

Wed, 19 Nov 2025 00:00:00 +0000

Multigrid methods represent the state-of-the-art for solving large-scale linear systems arising from discretized partial differential equations, offering optimal computational complexity for many problem classes.

Established implementations such as pyAMG and AMG.jl provide robust solvers but lack two critical capabilities for modern scientific machine learning: GPU acceleration and automatic differentiation compatibility. These features are essential for scientific machine learning workflows where differentiable simulation components (e.g., neural networks embedded in physical models) require efficient iterative solves with gradient backpropagation for end-to-end optimization.

This project aims to develop an algebraic multigrid (AMG) solver in JAX that natively supports automatic differentiation and GPU acceleration. The work involves analyzing existing Python and Julia implementations to design an architecture compatible with JAX’s functional programming paradigm and just-in-time compilation model. A successful implementation could have substantial impact on the JAX ecosystem, from accelerating finite element packages to accelerating ecological connectivity analysis tools.

The project scope is flexible and can emphasize software engineering or algorithmic optimization depending on the student’s background and interests. Prior experience with JAX or advanced numerical linear algebra is beneficial but not required; students will gain deep expertise in iterative solvers, functional programming patterns, and best practices for scientific open-source software development.

Differentiable Programming for Differential Equations: A Review

Wed, 01 Jan 2025 00:00:00 +0000

Deep learning approximations for non-local nonlinear PDEs with Neumann boundary conditions

Thu, 05 May 2022 00:00:00 +0000

Episodic mineralising fluid injection through chemical shear zones

Mon, 01 Jan 2018 00:00:00 +0000