Scientific machine learning

Multi-scale model for spatial biodiversity estimation.

Forecasting alien invasive species range dynamics with GNNs.

A Julia library for building and training hybrid dynamic models that combine mechanistic and data-driven components.

Neuron activity, fish stocks, viral spread, El Niño oscillations, energy demand: these complex phenomena show remarkable patterns that continue to fascinate scientists. Analyzing these dynamics through scientific models (that is, models embedding domain knowledge; e.

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.

In this tutorial, you will learn about different techniques to infer parameters of a (differentiable) process-based model against data.