Advances in Machine Learning & Artificial Intelligence(AMLAI)

Department of Family Medicine, Dong-eui Medical Center, Busan 47227, Republic of Korea

Publications

• Research Article   
  Liquid Crystal-Driven Navier–Stokes and Riemann Zeta Function Transformations as Solutions to Hardware Limitations in Graphene-Based Spintronic Neuromorphic Ising Machines
  Author(s): Chur Chin*
  
  The convergence of graphene-based spintronics, stochastic neurons, Ising machines, and neuromorphic architectures represents a transformative frontier in computing hardware. However, this convergence confronts fundamental physical and computational limitations: thermal decoherence, the von Neumann memory wall, nonlinear intractability of governing partial differential equations, and insufficient entropy generation for true random number synthesis. This paper proposes a novel theoretical and engineering framework in which liquid crystal (LC) systems governed by Navier–Stokes fluid dynamics for anisotropic media serve as a room-temperature physical computing substrate capable of resolving these hardware constraints. By transforming the Navier–Stokes equations (NSE) through Riemann zeta function spectral mappings, we establish that the eigen spectrum of the LC Stokes operator.. Read More»
  

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