The interplay between tectonic deformation, fluid circulation, and rock rheology is fundamental to understanding lithospheric evolution, seismic cycles, and resource formation. While traditional structural geology has established robust kinematic frameworks, quantifying the dynamic feedbacks among stress, fluid pressure, and rheological weakening remains a frontier challenge. Fortunately, rapid advances in Earth observation, geophysics, and geochronology have generated unprecedented multi-source data. Concurrently, high-resolution numerical modelling and Artificial Intelligence (AI) are revolutionizing our ability to decode these complex, non-linear processes across scales from grain to orogen.

This session convenes researchers bridging field-based structural analysis, experimental petrology, and computational geoscience. We emphasize big-data-driven and AI-assisted approaches to integrate multi-source observations into physically consistent interpretations of tectonic-fluid and rheological systems. We welcome contributions on topics including, but not limited to: 1) Deformation and rheology: brittle-ductile transitions, strain localization, flow laws, and lithospheric weakening mechanisms. 2) Fluid-rock interaction: metamorphic and/or magmatic fluid flow, mineral reactions, permeability evolution, and dynamic feedbacks on deformation. 3) Numerical and analogue modelling: multi-physics or multi-scale simulations of deformation-fluid interactions and parameter benchmarking. 4) AI and machine learning: automated recognition of microstructures, fault architectures, and fluid inclusions from remote sensing, geophysical, or imagery data; AI-assisted data mining and uncertainty quantification. 5) Big-data integration: geospatial, geophysical, and experimental database construction; data-driven discovery and hypothesis generation. 6) Applications: orogens, rifts, shear zones, and cratons; implications for seismic hazard, mineral resources, and energy exploration.