Researchers build physics-informed surrogate model for Mars' nightside thermosphere

A new arXiv paper introduces a multi-task surrogate model that combines physical constraints with machine learning to simulate the Martian nightside thermosphere. The problem is difficult because direct measurements are sparse and transport, magnetic, and seasonal effects interact strongly, so purely data-driven approaches can produce unphysical outputs such as reversed density trends. Embedding physics into the training process aims to keep the model's predictions consistent with known atmospheric behavior.