2 citations · 4 across the 6 of their papers we have counts for
6 papers
Differentiable FEM versus PINN for pavement layer inverse analysis: enforcing physics as hard or soft constraints
Yongjin Choi, Hyeonbin Moon, Seunghwa Ryu
Automatic-differentiation-based inverse analysis methods, including physics-informed neural networks (PINNs) and differentiable programming, have recently shown great promise due t…
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…
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…
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…
Graph Neural Network-based surrogate model for granular flows
Yongjin Choi, Krishna Kumar
Accurate simulation of granular flow dynamics is crucial for assessing various geotechnical risks, including landslides and debris flows. Granular flows involve a dynamic rearrange…
A machine learning approach to predicting pore pressure response in liquefiable sands under cyclic loading
Yongjin Choi, Krishna Kumar
Shear stress history controls the pore pressure response in liquefiable soils. The excess pore pressure does not increase under cyclic loading when shear stress amplitude is lower…