1 citations · 1 across the 3 of their papers we have counts for
3 papers
physics.geo-ph2024★ 1 cited
Inverse analysis of granular flows using differentiable graph neural network simulator
Yongjin Choi, Krishna Kumar
Inverse problems in granular flows, such as landslides and debris flows, involve estimating material parameters or boundary conditions based on target runout profile. Traditional h…
physics.geo-ph2023
Three-dimensional granular flow simulation using graph neural network-based learned simulator
Yongjin Choi, Krishna Kumar
Reliable evaluations of geotechnical hazards like landslides and debris flow require accurate simulation of granular flow dynamics. Traditional numerical methods can simulate the c…
physics.geo-ph2023
Accelerating Particle and Fluid Simulations with Differentiable Graph Networks for Solving Forward and Inverse Problems
Krishna Kumar, Yongjin Choi
We leverage physics-embedded differentiable graph network simulators (GNS) to accelerate particulate and fluid simulations to solve forward and inverse problems. GNS represents the…