Physics-Informed Multi-Task Surrogate Model for the Martian Nightside Thermosphere
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 prio...
| Authors | Sergey Nikiforov |
|---|---|
| Primary category | astro-ph.EP |
| Published | 9 September 2026 |
| Last updated at source | 9 September 2026 |
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