396 citations · 481 across the 7 of their papers we have counts for
6 papers · 1 filter
Learning Macroscopic Dynamics without Reconstructing Microscopic States
Zhichao Han, Yue Zhao, Qianxiao Li
Modeling the temporal evolution of macroscopic properties of complex systems is an important scientific task. To predict this evolution without full microscopic simulation, a commo…
Learning Permutation-invariant Macroscopic Dynamics
Zhichao Han, Mengyi Chen, Qianxiao Li
Accurately modeling the macroscopic dynamics of high-dimensional microscopic systems is of broad interest across the sciences. Many data-driven approaches learn a low-dimensional l…
Collective Relational Inference for learning heterogeneous interactions
Zhichao Han, Olga Fink, David S. Kammer
Interacting systems are ubiquitous in nature and engineering, ranging from particle dynamics in physics to functionally connected brain regions. These interacting systems can be mo…
Learning Physics-Consistent Particle Interactions
Zhichao Han, David S. Kammer, Olga Fink
Interacting particle systems play a key role in science and engineering. Access to the governing particle interaction law is fundamental for a complete understanding of such system…
Predicting Path Failure In Time-Evolving Graphs
Jia Li, Zhichao Han, Hong Cheng +4
In this paper we use a time-evolving graph which consists of a sequence of graph snapshots over time to model many real-world networks. We study the path classification problem in…
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
Qimai Li, Zhichao Han, Xiao-Ming Wu
Many interesting problems in machine learning are being revisited with new deep learning tools. For graph-based semisupervised learning, a recent important development is graph con…