AI RESEARCH
HGAN-SDEs: Learning Neural Stochastic Differential Equations with Hermite-Guided Adversarial Training
arXiv CS.LG
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ArXi:2512.20272v2 Announce Type: replace Neural Stochastic Differential Equations (Neural SDEs) provide a principled framework for modeling continuous-time stochastic processes and have been widely adopted in fields ranging from physics to finance. Recent advances suggest that Generative Adversarial Networks (GANs) offer a promising solution to learning the complex path distributions induced by SDEs. However, a critical bottleneck lies in designing a discriminator that faithfully captures temporal dependencies while remaining computationally efficient.