Surrogate models are simplified representations of complex real-world processes or systems, often used to approximate outputs of expensive simulations or data-driven methods. These models help reduce computation time and resources while maintaining a level of accuracy that is sufficient for analysis and decision-making. They are particularly valuable in scenarios where direct evaluation of a model is computationally prohibitive.
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