Universal approximation property of neural stochastic differential equations

Anna P. Kwossek; David J. Prömel; Josef Teichmann

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arXiv · 2503.16696

Universal approximation property of neural stochastic differential equations

Anna P. Kwossek·David J. Prömel·Josef Teichmann

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Abstract

We identify various classes of neural networks that are able to approximate continuous functions locally uniformly subject to fixed global linear growth constraints. For such neural networks the associated neural stochastic differential equations can approximate general stochastic differential equations, both of It\^o diffusion type, arbitrarily well. Moreover, quantitative error estimates are derived for stochastic differential equations with sufficiently regular coefficients.

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Anna P. Kwossek, David J. Prömel, Josef Teichmann. 2025-03-20. Universal approximation property of neural stochastic differential equations. https://arxiv.org/abs/2503.16696

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