AI RESEARCH
Neural Ordinary Differential Equations for Modeling Socio-Economic Dynamics
arXiv CS.LG
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ArXi:2604.00632v1 Announce Type: cross Poverty is a complex dynamic challenge that cannot be adequately captured using predefined differential equations. Nowadays, artificial machine learning (ML) methods have nstrated 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.