Physics-Based and AI-Driven HPC Workflows for Geophysical Hazards in GANANA project

The GANANA project is an EU–India initiative that builds on three pillars: geohazards, weather and climate and life sciences, each linked to a EuroHPC Center of Excellence (CoE). In particular, the ChEESE-2P CoE pillar  advances the use of High-Performance Computing (HPC) for geophysical hazard assessment and risk mitigation. It harnesses flagship HPC codes to deliver integrated, physics-based and data-driven solutions for earthquakes, tsunamis, smoke dispersion, and cascading hazards, with a strong focus on urgent computing,operational readiness and rapid response. We present GANANA’s high-level framework and first results across three core geophysical hazard domains. For earthquakes, urgent computing workflows enable near-real-time ground-shaking simulations using physics-based solvers, supporting rapid impact assessment for civil protection. These workflows are complemented by Artificial Intelligence / ML techniques  for seismic data monitoring, where deep-learning pipelines automate event detection, phase picking, and magnitude estimation, and are tightly integrated with physics-based simulations to enhance robustness in data-scarce and tectonically complex regions. For tsunamis, GANANA extends established HPC workflows for rapid forecasting and high-resolution inundation mapping, triggered by seismic events, with particular emphasis on operational applicability and transferability to new coastal regions. 

The workflow focused on wildfire spread and smoke dispersion, aims to develop an integrated forecasting system for urgent computing applications built upon expertise on the development of HPC codes for Numerical Weather Prediction (NWP) and atmospheric dispersion models. A defining feature of GANANA is its structured, bidirectional exchange of codes, expertise, and operational practices between Europe and India, enabling the adaptation, validation, and deployment of advanced HPC technologies in diverse geographical and institutional contexts. 

A key aspect of the project is also the cascading hazard - framework. Preliminary demonstrators show that this exchange significantly improves model performance, interoperability, and time-to-solution, while simultaneously fostering capacity building and shared ownership of advanced HPC tools. GANANA thus illustrates how sustained international collaboration can transform mature exascale-ready codes into scalable, user-oriented systems for geophysical hazard forecasting and early warning.