Neural Stochastic Differential Equation

Neural Stochastic Differential Equations (Neural SDEs) are a class of machine learning models that use neural networks to parameterize the drift and diffusion terms of stochastic differential equations, enabling the modeling of complex, continuous-time stochastic processes. Current research focuses on developing efficient training methods, improving model robustness and stability, particularly for irregularly sampled or noisy data, and exploring various architectures like Langevin dynamics and Volterra equations to handle path-dependent dynamics and diverse data types. These models are proving valuable for diverse applications, including time series forecasting, generative modeling, data assimilation, and the analysis of complex systems in fields such as finance, physics, and biology, offering advantages in handling uncertainty and incorporating prior knowledge.