Author

Zhe-Qi Shen

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Open access Oct 2026

Physics-informed hybrid neural networks with data constraints: A case study on lorenz63.

Physics-Informed Neural Networks (PINNs) are a hybrid modeling approach that integrates physical priors into neural networks. By encoding governing equations as soft constraint terms into the loss function, this method accomplishes organic integration of data-driven approaches with physical laws. PINNs demonstrate sign...

Yao Xiao, You-Min Tang, Yi Li et al. · Neural Networks · 0 citations

Preprint Aug 2026

A score-based particle flow filter for non-Gaussian data assimilation in high-dimensional chaotic systems

Current particle flow filters rely on Gaussian prior assumptions that fail to capture the non-Gaussian attractor structure of chaotic systems. This study proposes a Score-based Particle Flow Filter (Score-PFF) that replaces the parametric prior gradient with a neural network-learned score function via denoising score m...

Zhe-Qi Shen, You-Min Tang, Yue-Wei Fang · 0 citations