Physics-informed model predicts Mars nightside atmospheric gases reliably
Physics-Informed Multi-Task Surrogate Model for the Martian Nightside Thermosphere
Machine Learning
Summary
Understanding the atmosphere on Mars's night side is hard because there aren’t many direct measurements and many processes are linked. Purely data-based methods sometimes make unrealistic predictions like densities that don’t decrease with altitude. The authors created a neural network that predicts the amounts of four gases at once, using over ten years of spacecraft data. They included physics rules to stop impossible results and found it improved the model’s realism without losing accuracy. This model can quickly recreate the state of Mars’s nightside upper atmosphere with better vertical consistency.
What this means in practice
- •For space mission planners: Use improved atmospheric density predictions to better design Mars spacecraft trajectories and communication strategies during nighttime orbits.
- •For climate model developers: Integrate a physics-informed surrogate model that captures Mars nightside atmospheric composition for faster and more consistent simulations.
Authors
Sergey Nikiforov
Abstract
Modeling the Martian nightside thermosphere remains challenging due to sparse in situ sampling and strong coupling among transport, magnetic, and seasonal processes. Purely data-driven models can produce non-physical artifacts, such as density inversions, in poorly sampled altitude regimes. We present a multi-task physics-informed neural network that simultaneously predicts the base-10 logarithmic densities of four neutral species (O, CO$_2$, N$_2$, and Ar) using more than a decade of MAVEN/NGIMS observations (MY 32-38, 2014-2025). A shared backbone learns a common representation of the nightside thermospheric state and branches into species-specific output heads. A weak monotonicity prior is incorporated via automatic differentiation by penalizing positive vertical gradients in logarithmic density. Experiments using an orbit-disjoint train/validation/test split show that physics-informed regularization substantially reduces non-physical inversions while preserving predictive skill and slightly improving it in the best-performing configuration, as measured by RMSE, MAE, and $R^2$. The resulting model provides a computationally efficient surrogate for nightside thermospheric reconstruction with improved vertical consistency.