Universal approximation property of neural stochastic differential equations
Not on the map. Why?
Abstract (arXiv)
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 Ito diffusion type, arbitrarily well. Moreover, quantitative error estimates are derived for stochastic differential equations with sufficiently regular coefficients.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 11 of its 26 references that are in the map. First posted on arXiv 20 Mar 2025.
Builds on
11 of 26 references.
References · 11
- 2020Scalable Gradients for Stochastic Differential Equationscited by 271
- 2019Universal Approximation with Deep Narrow Networkscited by 200
- 2019Neural Jump Stochastic Differential Equationscited by 150
- 2019Neural SDE: Stabilizing Neural ODE Networks with Stochastic Noisecited by 101
- 2021Efficient and Accurate Gradients for Neural SDEscited by 71
- 2023Non-adversarial training of Neural SDEs with signature kernel scorescited by 37
- 2020A generative adversarial network approach to calibration of local stochastic volatility modelscited by 29
- 2021Arbitrage-free neural-SDE market modelscited by 19
- 2023Global universal approximation of functional input maps on weighted spacescited by 19
- 2023FuNVol: A Multi-Asset Implied Volatility Market Simulator using Functional Principal Components and Neural SDEscited by 6
- 2022Hedging option books using neural-SDE market modelscited by 2
Built on by
Citing papers per year: 2026: 2.
2 papers.