Neural potential field for high-fidelity gravity modeling and density inversion of irregular small celestial bodies

Key Points

• High-fidelity gravity modeling improves understanding of irregular celestial bodies' structures, and density inversion techniques reveal underlying material properties.
• The study achieves a significant improvement in modeling accuracy through a neural potential field approach, yielding insights into celestial density variations.
• Analysis employs advanced computational algorithms to derive gravitational fields from observational data, applying this to irregular body shapes, enhancing precision.
• Findings support the use of neural potential fields for space missions, as accurate modeling is crucial for understanding small celestial bodies, guiding exploration efforts.