The Computational Geomechanics & Particulate Systems Lab develops and applies advanced computational methods to solve critical engineering challenges. By integrating numerical simulations, such as the discrete element method and finite element method, with modern techniques like image processing and machine learning, we model and predict the behavior of geo- and particulate materials across all scales, from individual particles to large-scale engineering systems they form or interact with.
Core Research Areas
Natural GeoHazard Assessment
Conducting data-driven regional liquefaction hazard assessment and developing multiscale random field-based mapping techniques.
Geomaterial & Extraterrestrial Regolith
Characterizing and modeling geomaterials and extraterrestrial regolith and their interaction with engineering tools.
Biomass Particulate Mechanics & Modeling
Developing novel discrete element models, informed by multiscale experimental characterization, to understand particle attributes and variability for bioenergy applications.
Image Processing & Machine Learning
Integrating novel machine learning and image processing techniques with advanced numerical models for enhanced material characterization and predictive modeling.