Recent advances in physical and statistical modeling based on seismicity patterns provide new insights into the preparation of large earthquakes and the temporal, spatial, and magnitude evolution of seismicity.
Improvements in monitoring technologies now deliver seismic data of unprecedented quality and quantity. Earthquake catalogs are more complete and accurate than ever, and many are now publicly available, enabling analysis of understudied regions and expanding global knowledge. New-generation catalogs, sometimes compiled with machine learning, reveal seismicity structures in ways not previously possible.
Additionally, geodetic, geological, and geochemical data, fluid analyses, laboratory experiments, and earthquake simulators generating synthetic catalogs help refine models and test hypotheses. Integrating these multidisciplinary perspectives enhances our understanding of earthquake generation.
To exploit these datasets, statistical approaches and machine learning are essential. These tools uncover hidden relationships and clustering and address challenges of data inhomogeneity, paving the way for deeper understanding and more robust forecasting.
We invite contributions on developments in physical and statistical modeling and machine learning, including:

• Spatial, temporal, and magnitude properties of earthquake statistics
• Earthquake clustering analyses
• Earthquake forecasting, including related software and methods
• Effects of fluid diffusion and geodetic deformation on seismicity
• Applications in understudied regions (e.g., Africa, Southeast Asia)
• Quantitative testing, data requirements and validation of models
• Machine learning and pattern recognition applied to seismic data
• Model uncertainty quantification
• Reliability and completeness of catalogs
• Time-dependent hazard assessment