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

Towards a New Mathematical Model for Understanding the Non-Linear Self-Organization of the Universe: AI-Based Analysis of Astronomical Data By: Jalal Y A Khawaldeh | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 31 March 2025 V1 Latest version Share on Towards a New Mathematical Model for Understanding the Non-Linear Self-Organization of the Universe: AI-Based Analysis of Astronomical Data By: Jalal Y A Khawaldeh Author : Jalal Khawaldeh 0009-0003-7872-1967 [email protected] Authors Info & Affiliations https://doi.org/10.22541/au.174345558.82494066/v1 242 views 126 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract 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. Supplementary Material File (towards a new mathematical model for understanding the non-linear self-organization of the universe- ai-based analysis of astronomical data- doi jalal khawaldeh.pdf) Download 4.31 MB Information & Authors Information Version history V1 Version 1 31 March 2025 Copyright This work is licensed under a Creative Commons Attribution 4.0 International License Keywords alma observations artificial intelligence in astrophysics astrophysical patterns cosmic code data analysis deep space structures fuzzy dark matter gaia data analysis galaxy evolution gravitational dynamics jalal y a khawaldeh: conceptualization machine learning in cosmology mathematical modeling in astronomy model development non-linear universe quantum astrophysics random forest analysis self-organizing systems theoretical analysis. 2. funding statement cosmic self-organization writing-original draft writing-review and editing Authors Affiliations Jalal Khawaldeh 0009-0003-7872-1967 [email protected] View all articles by this author Metrics & Citations Metrics Article Usage 242 views 126 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Jalal Khawaldeh. Towards a New Mathematical Model for Understanding the Non-Linear Self-Organization of the Universe: AI-Based Analysis of Astronomical Data By: Jalal Y A Khawaldeh. Authorea . 31 March 2025. DOI: https://doi.org/10.22541/au.174345558.82494066/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . 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