3 papers
physics.geo-ph2026
Differentiable graph neural network simulator for forward and inverse modeling of multi-layered slope system with multiple material properties
Yongjin Choi, Jorge Macedo, Chenying Liu
Graph neural network simulators (GNS) have emerged as a computationally efficient tool for simulating granular flows. Previous efforts have been limited to simplified homogeneous g…
cs.LG2026
Operator Learning for Consolidation: An Architectural Comparison for DeepONet Variants
Yongjin Choi, Chenying Liu, Jorge Macedo
Deep Operator Networks (DeepONets) have emerged as a powerful surrogate modeling framework for learning solution operators in PDE-governed systems. While their use is expanding acr…
physics.geo-ph2026
Differentiable Graph Neural Network Simulator for the Back-Analysis of Post-Liquefaction Residual Strength from Flow Failure Runout
Yongjin Choi, Jorge Macedo
This study introduces Differentiable Graph Neural Network Simulators (Diff-GNS) as a physics-informed and automated framework for estimating post-liquefaction residual strengths ($…