Software
Up to now, I have built and published two Python packages.
NNBMA
NNBMA (for Neural Network-Based Model approximation) is a Python package that creates and trains neural networks to approximate complex and slow numerical models. The goal is then to plug these neural networks in inference schemes, such as Markov chain Monte Carlo (MCMC) algorithms.
This package was developed in close collaboration with Lucas Einig.
Beetroots
Beetroots (for BayEsian infErence with spaTial Regularization of nOisy multi-line ObservaTion mapS) is a Python package that performs Bayesian inference of physical parameters from multispectral-structured cubes with a dedicated sampling algorithm. Thanks to this sampling algorithm, beetroots provides maps of credibility intervals along with estimated maps.