7 citations · 7 across the 8 of their papers we have counts for
9 papers
Inferring Network Evolutionary History via Structure-State Coupled Learning
En Xu, Shihe Zhou, Huandong Wang +2
Inferring a network's evolutionary history from a single final snapshot with limited temporal annotations is fundamental yet challenging. Existing approaches predominantly rely on…
Zero-Shot Forecasting of Network Dynamics through Weight Flow Matching
Shihe Zhou, Ruikun Li, Huandong Wang +1
Forecasting state evolution of network systems, such as the spread of information on social networks, is significant for effective policy interventions and resource management. How…
Beyond Accuracy and Complexity: The Effective Information Criterion for Structurally Stable Symbolic Regression
Zihan Yu, Guanren Wang, Jingtao Ding +2
Symbolic regression (SR) traditionally balances accuracy and complexity, implicitly assuming that simpler formulas are structurally more rational. We argue that this assumption is…
WeightFlow: Learning Stochastic Dynamics via Evolving Weight of Neural Network
Ruikun Li, Jiazhen Liu, Huandong Wang +2
Modeling stochastic dynamics from discrete observations is a key interdisciplinary challenge. Existing methods often fail to estimate the continuous evolution of probability densit…
A Survey of Physics-Informed AI for Complex Urban Systems
En Xu, Huandong Wang, Yunke Zhang +8
Urban systems are typical examples of complex systems, where the integration of physics-based modeling with artificial intelligence (AI) presents a promising paradigm for enhancing…
Beyond Equilibrium: Non-Equilibrium Foundations Should Underpin Generative Processes in Complex Dynamical Systems
Jiazhen Liu, Ruikun Li, Huandong Wang +4
This position paper argues that next-generation non-equilibrium-inspired generative models will provide the essential foundation for better modeling real-world complex dynamical sy…
Sparse Diffusion Autoencoder for Test-time Adapting Prediction of Complex Systems
Jingwen Cheng, Ruikun Li, Huandong Wang +1
Predicting the behavior of complex systems is critical in many scientific and engineering domains, and hinges on the model's ability to capture their underlying dynamics. Existing…
Generative Adaptation of Dynamics to Environmental Shifts via Weight-space Diffusion
Ruikun Li, Huandong Wang, Jingtao Ding +3
Data-driven dynamics prediction often fails under environmental shifts, while traditional fine-tuning remains computationally prohibitive for hardware-constrained or data-scarce ap…
A Comprehensive Survey on Artificial Intelligence for Complex Network: Potential, Methodology and Application
Jingtao Ding, Chang Liu, Yu Zheng +8
Complex networks pervade various real-world systems, from the natural environment to human societies. The essence of these networks is in their ability to transition and evolve fro…