6 citations · 17 across the 19 of their papers we have counts for
4 papers · 1 filter
Operator-learning-inspired Modeling of Neural Ordinary Differential Equations
Woojin Cho, Seunghyeon Cho, Hyundong Jin +6
Neural ordinary differential equations (NODEs), one of the most influential works of the differential equation-based deep learning, are to continuously generalize residual networks…
Learning Flexible Body Collision Dynamics with Hierarchical Contact Mesh Transformer
Youn-Yeol Yu, Jeongwhan Choi, Woojin Cho +9
Recently, many mesh-based graph neural network (GNN) models have been proposed for modeling complex high-dimensional physical systems. Remarkable achievements have been made in sig…
Graph Convolutions Enrich the Self-Attention in Transformers!
Jeongwhan Choi, Hyowon Wi, Jayoung Kim +4
Transformers, renowned for their self-attention mechanism, have achieved state-of-the-art performance across various tasks in natural language processing, computer vision, time-ser…
Reduced-order modeling for parameterized PDEs via implicit neural representations
Tianshu Wen, Kookjin Lee, Youngsoo Choi
We present a new data-driven reduced-order modeling approach to efficiently solve parametrized partial differential equations (PDEs) for many-query problems. This work is inspired…