Our research group is dedicated to exploring advanced numerical methods and innovative techniques that bridge the gap between traditional simulations and modern data-driven approaches. Below are our primary areas of focus:
SciML: Scientific Machine Learning
Our research on SciML focuses on graph network simulators and differentiable programming for solving inverse problems, discovering underlying physics using a data-driven approach, and AI-accelerated simulations.
MPM: Material Point Method
The Material Point Method (MPM) is a hybrid Eulerian-Lagrangian approach, which uses moving material points and computational nodes on a background mesh. This approach is very effective particularly in the context of large deformations.
LBM/DEM: Lattice Boltzmann & Discrete Element Method
The Lattice Boltzmann equation Method (LBM) is a meso-scale fluid solver for modeling grain-scale fluid flow. The Discrete-Element Method (DEM) is coupled with LBM to model soil-fluid interactions at particulate scale.
Research Spotlights
Micromechanics of soil-water-characteristic curve
LBM simulations of hysteresis in SWCC.
In Situ Viz with Material Point Method
Real-time rendering of MPM landslide simulations