Neural Ordinary Differential Equations for Modeling Socio-Economic Dynamics
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 architectur...
| Authors | Sandeep Kumar Samota, Snehashish Chakraverty, Narayan Sethi |
|---|---|
| Primary category | math.DS |
| Published | 1 April 2026 |
| Last updated at source | 1 April 2026 |
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