Physics Informed Neural Networks for Anisotropic Cosmology Solving Bianchi Type-I Dynamics with Quintessence Dark Energy

Physics Informed Neural Networks for Anisotropic Cosmology Solving Bianchi Type-I Dynamics with Quintessence Dark Energy | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Physics Informed Neural Networks for Anisotropic Cosmology Solving Bianchi Type-I Dynamics with Quintessence Dark Energy Muhammad Zeeshan Ashraf, H. Rizwana Kausar, Amal Majid This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8723102/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract We introduce the Physics Informed Neural Networks (PINNs) model to solve the coupled Einstein Klein-Gordon system of equations describing the Bianchi Type-I anisotropic universe with a quintessence scalar field. Taking the full set of gravitational and dynamical constraints into the loss function, our net work is independent of any observational data or numerical discretizations to learn physically consistent cosmological evolutions. We are incorporating the governing equations, including the EFEs, directional Hubble rates, shear dynamics, and scalar field evolution in a unified PINNs architecture. The well-trained model reproduces anisotropic expansion, scalar field dynamics, and shear decay with very high precision in comparison to fourth-order Runge–Kutta (RK4) integrations. We find that PINNs are able to accu rately represent the nonlinear structure of anisotropic cosmology with smooth, differentiable, and globally defined solutions. This work provides a direct link between artificial intelligence and the fundamental physics of the early universe, whilst also demonstrating that PINNs can indeed serve as a powerful probe for anisotropic spacetime, dark-energy models, and extensions to modified gravity. Physical sciences/Physics Physical sciences/Physics/Chemical physics Physics-Informed Neural Networks Bianchi Type-I Anisotropic Cosmology Quintessence Einstein Field Equations PyTorch Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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