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Abstract
Complexity reduction techniques are increasingly vital in interdisciplinary modeling and simulation. These techniques aim to balance fidelity and computational efficiency by reducing the dimensionality, data volume, and system intricacy without significantly compromising accuracy. Their importance is magnified by the surge in high-dimensional data and the demand for real-time predictive models in fields like climate science, systems biology, and multi-physics engineering.
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