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Abstract A fundamental problem in science is identifying underlying patterns of complex systems in the form of concise mathematical formulas. Current Artificial Intelligence (AI)-based methods have shown strong performance in single-scale systems, yet remain limited in identifying scale-specific formulas in multiscale complex systems.
With the development of advanced algorithms, computing capabilities, and available datasets, machine learning (ML) has been widely adopted to solve real-world problems in various application domains. The success of ML often relies on large amounts of application-specified training data, especially for large models such as ChatGPT.
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