Neural stochastic Volterra equations: learning path-dependent dynamics
Machine Learning
2025-12-30 v3 Probability
Machine Learning
Abstract
Stochastic Volterra equations (SVEs) serve as mathematical models for the time evolutions of random systems with memory effects and irregular behaviour. We introduce neural stochastic Volterra equations as a physics-inspired architecture, generalizing the class of neural stochastic differential equations, and provide some theoretical foundation. Numerical experiments on various SVEs, like the disturbed pendulum equation, the generalized Ornstein--Uhlenbeck process, the rough Heston model and a monetary reserve dynamics, are presented, comparing the performance of neural SVEs, neural SDEs and Deep Operator Networks (DeepONets).
Cite
@article{arxiv.2407.19557,
title = {Neural stochastic Volterra equations: learning path-dependent dynamics},
author = {Martin Bergerhausen and David J. Prömel and David Scheffels},
journal= {arXiv preprint arXiv:2407.19557},
year = {2025}
}
Comments
24 pages