Scientific framework
Four directions, all built on the same principle: physics-guided AI and high-resolution imaging applied to real subsurface problems.
The Institute's foundational methodology work: combining physical laws with AI to build subsurface models that stay interpretable rather than just accurate on paper.
Physics-informed neural networks (PINNs), multi-dimensional forward modeling, HPC deployment, and automated processing of high-density datasets.
Modernizing mineral prospecting by combining physical geoscience constraints with AI-driven interpretation, rather than replacing one with the other.
Physics-guided AI architectures, joint inversion of magnetometry, gravimetry, and induced polarization data, and structural mapping in remote terrain.
Structural imaging and risk monitoring: active and passive wave-propagation physics combined with computational inversion to track subsurface change over time.
High-resolution seismic inversion, active/passive data processing, wavefield simulation, and forecasting for geological and environmental hazards.
Rigorous geophysical methods applied to near-surface engineering, environmental protection, and shallow-depth site investigations.
Groundwater exploration, near-surface safety and geotechnical assessment, environmental hazard mitigation, shallow-depth property mapping.
Academic & industrial synergy
Technical questions on our methods, publications, or ongoing themes (PINNs, DAS, inversion).
Advanced technical courses, computational workshops, and custom professional development.