[2604.00669] Embedded Variational Neural Stochastic Differential Equations for Learning Heterogeneous Dynamics

Computer Science > Machine Learning arXiv:2604.00669 (cs) [Submitted on 1 Apr 2026] Title:Embedded Variational Neural Stochastic Differential Equations for Learning Heterogeneous Dynamics Authors:Sandeep Kumar Samota, Reema Gupta, Snehashish Chakraverty View a PDF of the paper titled Embedded Variational Neural Stochastic Differential Equations for Learning Heterogeneous Dynamics, by Sandeep Kumar Samota and 2 other authors View PDF HTML (experimental) Abstract:This study examines the challenges of modeling complex and noisy data related to socioeconomic factors over time, with a focus on data from various districts in Odisha, India. Traditional time-series models struggle to capture both trends and variations together in this type of data. To tackle this, a Variational Neural Stochastic Differential Equation (V-NSDE) model is designed that combines the expressive dynamics of Neural SDEs with the generative capabilities of Variational Autoencoders (VAEs). This model uses an encoder and a decoder. The encoder takes the initial observations and district embeddings and translates them into a Gaussian distribution, which determines the mean and log-variance of the first latent state. Then the obtained latent state initiates the Neural SDE, which utilize neural networks to determine the drift and diffusion functions that govern continuous-time latent dynamics. These governing functions depend on the time index, latent state, and district embedding, which help the model learn th...