Towards a New Mathematical Model for Understanding the Non-Linear Self-Organization of the Universe: AI-Based Analysis of Astronomical Data

The evolution of the universe has traditionally been modeled through linear frameworks, yet emerging evidence suggests that non-linear self-organization plays a crucial role in shaping cosmic structures. This study proposes a novel mathematical model of cosmic self-organization, incorporating machine learning techniques to analyze large-scale astronomical datasets from Gaia and ALMA. Using Random Forest regression, we uncover hidden non-linear patterns governing the distribution of matter and energy, indicating the presence of an intrinsic "cosmic code" that dictates galactic evolution. Our findings reveal that non-linear AI models significantly outperform traditional linear regression in predicting astrophysical phenomena, reinforcing the hypothesis that the universe follows a structured yet self-organizing mathematical framework. These insights provide a new direction for computational astrophysics, suggesting that future studies should explore deep learning models and real-time cosmic simulations to further validate this framework.