7 citations · 13 across the 9 of their papers we have counts for
10 papers
Decoupling Perception from Description: Computation-Grounded Representation Alignment between Multivariate Time Series and Language
Xinran Feng, Yi Xie, Chao Zhang +4
Training multimodal models to align time series with language runs into a self-supervision trap. The usual recipe asks an LLM to read a series and write a description, so label qua…
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…
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…
Predicting the Dynamics of Complex System via Multiscale Diffusion Autoencoder
Ruikun Li, Jingwen Cheng, Huandong Wang +2
Predicting the dynamics of complex systems is crucial for various scientific and engineering applications. The accuracy of predictions depends on the model's ability to capture the…
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…
Predicting the Energy Landscape of Stochastic Dynamical System via Physics-informed Self-supervised Learning
Ruikun Li, Huandong Wang, Qingmin Liao +1
Energy landscapes play a crucial role in shaping dynamics of many real-world complex systems. System evolution is often modeled as particles moving on a landscape under the combine…
Predicting Long-term Dynamics of Complex Networks via Identifying Skeleton in Hyperbolic Space
Ruikun Li, Huandong Wang, Jinghua Piao +2
Learning complex network dynamics is fundamental for understanding, modeling, and controlling real-world complex systems. Though great efforts have been made to predict the future…
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…