Author

Shengxin Kong

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Open access Aug 2026

DeepTL4SE: Deep Transfer Learning for Power System State Estimation via Physics-Informed Data Generation

Power system state estimation (PSSE) requires accurate and timely inference from noisy measurements, but large labeled operational datasets are often unavailable and deployment data may differ from offline training data. This paper presents Deep Transfer Learning for State Estimation (DeepTL4SE), which combines Physics...

Zhen-De Zhang, Xianglong Li, Shengxin Kong et al. · Energies · 0 citations

Preprint Aug 2026

Improving Auto-Design of Neural PDE Solvers with a Domain-Specific Language

ADSL-PDE improves both search efficiency and optimization stability, achieving an improvement of more than 52% within the first ten evolution iterations, suggesting a broader principle for LLM-driven auto-design: effective agents do not merely require stronger reasoning, but rather a search representation that concentr...

Shengxin Kong, Liwen Xu, Jingwen Fu · 1 citation