This study introduces a diffusion-based modeling framework designed to analyze systemic risk propagation across interconnected financial and energy systems. Modern economic structures consist of highly interdependent sectors where shocks originating in one domain may rapidly transmit to other sectors through complex network connections. Inspired by diffusion dynamics in physics, the proposed model applies network-based diffusion equations to simulate how risk intensity evolves across interconnected nodes representing economic sectors such as energy markets, logistics networks, inflation dynamics, banking systems, and capital markets. The framework integrates three analytical modules: an event detection mechanism for identifying potential shocks, a diffusion simulation engine that models risk transmission across network structures, and a strategic impact module that evaluates systemic amplification points and propagation pathways. By combining diffusion mathematics, network theory, and macro-financial system analysis, the model provides a dynamic perspective on systemic risk monitoring and forward-looking risk propagation analysis. The framework aims to support improved financial stability assessment and strategic decision-making in highly interconnected global economic environments.

This research is part of an ongoing analytical initiative conducted within the AXION Global Research Division focusing on systemic risk, macro-financial stability, and interconnected economic systems. The study aims to develop conceptual and mathematical tools that improve the understanding of how localized disruptions propagate across complex economic networks. The proposed diffusion-based framework is designed as a theoretical and analytical model that can support future simulation platforms and decision-support tools. The model does not rely on proprietary institutional datasets and is intended as a conceptual research contribution to the fields of systemic risk analysis, network economics, and financial stability studies. Future research may extend this framework through empirical calibration using financial network data, energy market indicators, and macroeconomic variables. Additional work may also integrate computational simulations and machine learning techniques to enhance model calibration and predictive capabilities. This work contributes to the broader effort of developing dynamic analytical approaches for monitoring systemic risk propagation across multi-sector economic environments.