3 papers
cs.LG2026
A Physics-Regulated Neural Framework for Learning 3D Grain Growth Dynamics
Zhihui Tian, Kang Yang, Michael Tonks +2
Grain growth is governed by the reduction in grain boundary energy and exhibits well-established statistical scaling laws. Developing data-driven surrogates that preserve these phy…
cs.CV2026
MLE-UVAD: Minimal Latent Entropy Autoencoder for Fully Unsupervised Video Anomaly Detection
Yuang Geng, Junkai Zhou, Kang Yang +5
In this paper, we address the challenging problem of single-scene, fully unsupervised video anomaly detection (VAD), where raw videos containing both normal and abnormal events are…
cs.LG2026
Scaling Kinetic Monte-Carlo Simulations of Grain Growth with Combined Convolutional and Graph Neural Networks
Zhihui Tian, Ethan Suwandi, Tomas Oppelstrup +3
Graph neural networks (GNN) have emerged as a promising machine learning method for microstructure simulations such as grain growth. However, accurate modeling of realistic grain b…