DIRAC

Today, our atmospheric models are fit to the data using MCMC type approaches. This is sufficient if your atmospheric forward models are fast to run but convergence becomes problematic if this is not the case. This challenge looks at inverse modelling using machine learning. For more information on why we need your help, we provide more background in the about page and the documentation... Some of you may have realised that in 2022, we ran a similar challenge at NeurIPS. That challenge was a huge success and over 200 teams participated in solving our inverse modelling task. This year, we are taking it up a notch and are making the training set sparser, the chemistry more non-linear and the planet observations harder. Year by year we are getting closer to our goal of having highly realistic and robust ML models for our hardest cases. If you think you've done well last year, give this one a go!... Welcome to the Ariel Machine Learning Data Challenge. The Ariel Space mission is a..