Lightweight machine learning method enhances scalable structural inference and dynamic prediction accuracy

In recent strides within machine learning technology, particularly in reservoir computing (RC), notable advancements have been made in understanding complex systems across various domains. Researchers have been tirelessly innovating machine learning methods to analyze and forecast the dynamic behaviors of intricate systems using observed time series data. However, a pressing challenge persists: how to uphold a lightweight model while harnessing more structural information to achieve precise predictions of complex dynamics.

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