PDE foundation models are skillful AI weather emulators for the Martian atmosphere
2026-02-16 • Machine Learning
Machine Learning
AI summaryⓘ
The authors trained an AI model originally designed to solve math problems called partial differential equations (PDEs) so it could predict Martian weather. They improved a 2D model called Poseidon to work in 3D, keeping its previous learning intact. Using a lot of Martian weather data, they showed that their method predicts better weather patterns even when starting with limited initial info. This suggests that models trained on math equations can be adapted to real-world tasks with less data and computing power.
partial differential equationsfoundation modelsPoseidon modelMartian atmosphere3D model extensionweather emulationpretrainingfine-tuningsparse initial conditionsGPU compute
Authors
Johannes Schmude, Sujit Roy, Liping Wang, Theodore van Kessel, Levente Klein, Marcus Freitag, Eloisa Bentivegna, Robert Manson-Sawko, Bjorn Lutjens, Manil Maskey, Campbell Watson, Rahul Ramachandran, Juan Bernabe-Moreno
Abstract
We show that AI foundation models that are pretrained on numerical solutions to a diverse corpus of partial differential equations can be adapted and fine-tuned to obtain skillful predictive weather emulators for the Martian atmosphere. We base our work on the Poseidon PDE foundation model for two-dimensional systems. We develop a method to extend Poseidon from two to three dimensions while keeping the pretraining information. Moreover, we investigate the performance of the model in the presence of sparse initial conditions. Our results make use of four Martian years (approx.~34 GB) of training data and a median compute budget of 13 GPU hours. We find that the combination of pretraining and model extension yields a performance increase of 34.4\% on a held-out year. This shows that PDEs-FMs can not only approximate solutions to (other) PDEs but also anchor models for real-world problems with complex interactions that lack a sufficient amount of training data or a suitable compute budget.