Combining numerical CFD models and AI to enhance lava flow simulations

Lava flows are complex, non-Newtonian fluids with visco-thermal dependencies that can overcome barriers, form tunnels, and significantly impact surrounding areas. Understanding and predicting these flows are critical for quantifying volcanic hazards. Computational Fluid Dynamics (CFD) models are indispensable tools for simulating lava dynamics, but they often entail high computational costs, limiting their real-time applicability. To address these challenges, we propose an AI-enhanced CFD emulator for lava flows, designed to improve modeling efficiency while preserving accuracy. Our approach integrates AI with CFD to capture the visco-thermal properties of lava and its intricate dynamics, including phase transitions, particle solidification, and the influence of air on thermal behavior. The emulator has been validated through simulations of diverse physical scenarios, demonstrating its capability to generalize across varying conditions. Additionally, we conducted a sensitivity analysis, exploring the influence of key parameters, such as effusion rate, on lava flow evolution and eruption styles. By incorporating satellite-derived estimates, we provide insights into eruptive behaviors while minimizing the risks of field observations. Our results showcase the potential of combining AI, numerical models, and remote sensing to enhance traditional volcanic monitoring approaches. This hybrid methodology enables faithful, near real-time simulations of lava flows, offering valuable tools for hazard assessment and risk mitigation.