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Life sciences · Preprint
arXiv · September 9, 2026
Early or partial results. Treat as a signal, not a conclusion.
This is an unrefereed methods paper presenting a multi-task physics-informed neural network trained on MAVEN satellite observations to reconstruct Martian nightside thermospheric neutral densities. The authors show that incorporation of a weak monotonicity prior via automatic differentiation reduces non-physical density inversions while maintaining or slightly improving predictive metrics (RMSE, MAE, R²) on a held-out test set. The work demonstrates feasibility of physics regularization in data-driven atmospheric modeling but lacks external validation or comparison to established models.
Computational model development and validation study with orbit-disjoint train/validation/test split. MAVEN/NGIMS observations of the Martian nightside thermosphere, MY 32–38 (2014–2025); sparse in situ sampling.. Intervention: Physics-informed regularization via weak monotonicity prior (automatic differentiation penalizing positive vertical density gradients).. Compared with: Purely data-driven baseline (implicit; no explicit comparator model described)..
Physics-informed regularization substantially reduces non-physical vertical density inversions in poorly sampled regimes. Multi-task architecture with shared backbone and species-specific heads predicts O, CO₂, N₂, and Ar densities from MAVEN/NGIMS (MY 32–38, 2014–2025). Orbit-disjoint train/validation/test split shows preserved or slightly improved predictive skill (RMSE, MAE, R²) with monotonicity penalty applied.
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This is a methods/proof-of-concept study demonstrating a physics-informed machine-learning approach to modeling Martian thermosphere data, with no clinical or human health outcome and no comparison to an established standard or competing method.
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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.
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