A surrogate model is a simplified representation of a complex system or process, often used in fields like engineering and machine learning. It approximates the behavior of the original model, allowing for faster evaluations and predictions without needing to run expensive simulations or experiments. These models are particularly useful when dealing with high-dimensional data or when the original model is computationally intensive. By using techniques such as regression, neural networks, or Gaussian processes, surrogate models can provide insights and optimize designs efficiently while reducing time and resource consumption.