2 citations · 2 across the 2 of their papers we have counts for
3 papers
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…
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…
Overview and Introduction to Development of Non-Ergodic Earthquake Ground-Motion Models
Grigorios Lavrentiadis, Norman A. Abrahamson, Kuehn M. Nicolas +13
This paper provides an overview and introduction to the development of non-ergodic ground-motion models, GMMs. It is intended for a reader who is familiar with the standard approac…