Gravity Model

Gravity models aim to mathematically represent the gravitational field of celestial bodies or regions on Earth, crucial for applications ranging from spacecraft navigation to geophysical exploration. Current research focuses on improving model accuracy and efficiency through advanced algorithms, including machine learning techniques like Physics-Informed Neural Networks (PINNs) and Bayesian neural networks, and enhanced data processing methods such as Kriging interpolation. These improvements address challenges like handling sparse or noisy data, accurately modeling complex shapes and heterogeneous densities, and ensuring robustness in extrapolation. The resulting advancements have significant implications for autonomous navigation, resource exploration, and a deeper understanding of gravitational phenomena across diverse scales.