Neural networks for the Tamed-Milstein approximation of SDEs with additive symmetric jumps.

This work aims to estimate the drift and diffusion functions in stochastic differential equations (SDEs) driven by a special class of Lévy processes with finite jump intensity, using neural networks. We propose a framework that integrates the Tamed-M…

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