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October 20, 2025Open Access

Neural Networks for Tamed Milstein Approximation of SDEs with Additive Symmetric Jump Noise Driven by a Poisson Random Measure

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Neural networks successfully estimate drift and diffusion in stochastic differential equations, enhancing modeling capabilities.
The integration of the Tamed-Milstein scheme with neural networks enables sophisticated approximation of complex systems.
The methodology allows for non-parametric approximation of functions, dealing with state-dependent noise in stochastic settings.
The approach highlights a flexible alternative for estimating systems driven by Lévy processes, showing significant potential for inference.

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Framework employs neural networks to model drift and diffusion in stochastic differential equations, suggesting new inference methods.

Ramírez-González et al. (2025) studied this question.

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