Convergence dynamics of neural-network loss optimization
Mathematical Modeling and Validation of Convergence Dynamics for Neural Network Loss Optimization

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Convergence dynamics of neural-network loss optimization

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Single-physics models fail to describe the multiphysics, multi-time-scale behavior of Intrinsic Noise Transcription. We formulate the phenomenon as a coupled PDE/ODE system, discretize it using a finite-difference discretization with Crank-Nicolson time integration under an accelerated iteration scheme, and identify governing-term candidates via automated equation-form search, benchmarked by rediscovery of a known law. Validation is numerical (grid convergence and parameter sensitivity); external experimental validation is left as future work (measured ±50% sweep: κ +86.1%/-24.4%, γ -44.7%/+551.3%, D -6.4
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