Unified error analysis for learning fractional SDEs from discrete data yields convergence rates incorporating trajectory regularity, with neural network applications validated numerically.
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Error analysis for learning fractional stochastic differential equations with applications in neural approximations
Unified error analysis for learning fractional SDEs from discrete data yields convergence rates incorporating trajectory regularity, with neural network applications validated numerically.