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About

Mohamed Sassi is a researcher whose work bridges geophysics, energy science, and robotics, with a focus on understanding and quantifying fluid dynamics in complex natural systems. His key contributions center on the interplay between rock permeability and capillary pressure—two fundamental properties that govern fluid flow in porous media. In his highly cited 2021 paper, "Morphology Decoder," Sassi pioneered a machine learning-guided 3D vision approach to quantify heterogeneous rock permeability, using Lattice Boltzmann simulations to analyze nano- and micropore networks. This work, with 19 citations, offers a powerful tool for planetary surveillance and robotic functions by reducing the computational burden of millions of flow dynamics calculations. In a complementary study (17 citations), Sassi unraveled the co-determination of capillary pressure and permeability, solving a long-standing challenge in geodynamics by showing how knowing one property can predict the other. Beyond Earth sciences, he contributed to fusion energy research through the Ignitor Program, advancing high-field magnet technology for D-T ignition conditions. His work on modeling and control of a 6-DOF platform manipulator further showcases his versatility in robotics. With over 60 total citations, Sassi’s interdisciplinary impact is clear, offering students and researchers novel computational and physical insights into fluid flow, energy systems, and autonomous exploration.