A gauge-covariant stochastic neural field theory is introduced that derives the maximal Lyapunov exponent and amplification factor, showing finite-width effects as perturbative corrections to dressed kernels that leave the marginality condition unchanged for fixed kernel geometry.
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Dynamics-encoded deep learning approaches are developed for system identification and parameter estimation in dynamical systems using numerical discretization schemes.
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Gauge-covariant stochastic neural fields: Stability and finite-width effects
A gauge-covariant stochastic neural field theory is introduced that derives the maximal Lyapunov exponent and amplification factor, showing finite-width effects as perturbative corrections to dressed kernels that leave the marginality condition unchanged for fixed kernel geometry.
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Dynamics-Encoded Deep Learning for Robust System Identification and Parameter Estimation
Dynamics-encoded deep learning approaches are developed for system identification and parameter estimation in dynamical systems using numerical discretization schemes.