[2604.00632] Neural Ordinary Differential Equations for Modeling Socio-Economic Dynamics

Mathematics > Dynamical Systems arXiv:2604.00632 (math) [Submitted on 1 Apr 2026] Title:Neural Ordinary Differential Equations for Modeling Socio-Economic Dynamics Authors:Sandeep Kumar Samota, Snehashish Chakraverty, Narayan Sethi View a PDF of the paper titled Neural Ordinary Differential Equations for Modeling Socio-Economic Dynamics, by Sandeep Kumar Samota and 2 other authors View PDF HTML (experimental) Abstract:Poverty is a complex dynamic challenge that cannot be adequately captured using predefined differential equations. Nowadays, artificial machine learning (ML) methods have demonstrated significant potential in modelling real-world dynamical systems. Among these, Neural Ordinary Differential Equations (Neural ODEs) have emerged as a powerful, data-driven approach for learning continuous-time dynamics directly from observations. This chapter applies the Neural ODE framework to analyze poverty dynamics in the Indian state of Odisha. Specifically, we utilize time-series data from 2007 to 2020 on the key indicators of economic development and poverty reduction. Within the Neural ODE architecture, the temporal gradient of the system is represented by a multi-layer perceptron (MLP). The obtained neural dynamical system is integrated using a numerical ODE solver to obtain the trajectory of over time. In backpropagation, the adjoint sensitivity method is utilized for gradient computation during training to facilitate effective backpropagation through the ODE solver. Th...