9 papers
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…
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…
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…
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…
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…
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…