9 citations · 20 across the 15 of their papers we have counts for
4 papers · 1 filter
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…
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…