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cs.LG2025
Incomplete Data, Complete Dynamics: A Diffusion Approach
Zihan Zhou, Chenguang Wang, Hongyi Ye +2
Learning physical dynamics from data is a fundamental challenge in machine learning and scientific modeling. Real-world observational data are inherently incomplete and irregularly…
cs.LG2024
Generating Physical Dynamics under Priors
Zihan Zhou, Xiaoxue Wang, Tianshu Yu
Generating physically feasible dynamics in a data-driven context is challenging, especially when adhering to physical priors expressed in specific equations or formulas. Existing m…
cs.LG2024
On Diffusion Process in SE(3)-invariant Space
Zihan Zhou, Ruiying Liu, Jiachen Zheng +2
Sampling viable 3D structures (e.g., molecules and point clouds) with SE(3)-invariance using diffusion-based models proved promising in a variety of real-world applications, wherei…