Optical Fiber Coating Using Bayesian Distributed Back Propagation: Historical Development, Current State and Future Perspective

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ID: 312771
2025
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Abstract
Modeling magnetohydrodynamic (MHD) flows in double-layer optical fibre coatings poses significant computational challenges due to their nonlinear and anisotropic nature. Traditional computational fluid dynamics (CFD) techniques often struggle with scalability and precision in such high-dimensional systems. This paper presents a systematic review of Bayesian distributed backpropagation, highlighting its integration with neural networks to address uncertainty quantification and improve model generalization. The study reformulates key physical laws—Navier-Stokes with Lorentz force and Maxwell’s equations—within machine learning frameworks optimized via distributed Bayesian learning. Comparative analysis demonstrates that Bayesian methods outperform conventional backpropagation and optimization algorithms in accuracy and robustness, particularly under complex electromagnetic-fluid interactions. Nevertheless, high computational costs and convergence time remain major limitations, especially in real-time applications. The review identifies key breakthroughs in uncertainty modeling and intelligent neuro-structure optimization, offering practical relevance for optical fibre manufacturing. Future directions include hybrid Bayesian methods and scalable distributed learning strategies to address nonlinear, anisotropic systems more effectively and support broader industrial deployment of MHD flow simulation technologies.
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imported_1777056687_69ebbbaf3332b Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Saeed Ehsan Awan
Journal Journal of Computing & Biomedical Informatics
Year 2025
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/993
Keywords Keywords not found

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