Many of today’s societal needs such as mitigation of natural hazards, energy and environmental sustainability, development of resilient civil infrastructure, and accessing natural resources require studying the physical properties and processes of the Earth and geophysical systems across all scales from both scientific and technological perspectives. Moreover, natural hazards such as landslides, earthquakes, floods, and wildfires pose major threats to human lives and the critical infrastructure. Machine learning techniques and digital twin technology offer great potential for advanced management of geosystems and natural hazards. This mini-symposium aims to provide a forum to discuss recent advances in applications of Artificial Intelligence and Digital Twins to enhance monitoring and assessment of geosystems and infrastructure, as well as prediction and mitigation of natural hazards. The topics of interest include, but are not limited to: - Data-driven and physics-informed modeling of geosystems and natural hazards across scales - Advanced numerical modeling techniques for geosystems and natural hazards - Advances in sensing and monitoring techniques - Geohazards prediction and assessment - Data analytics in geosystems application - High-performance computing for digital twins - Reduced order modeling - Inverse modeling techniques - Probabilistic forecasting of natural hazards - Real-time assessment and monitoring of structures and infrastructure - Infrastructure maintenance and retrofitting