Surrogate Model
Surrogate Model is a simpler model that approximates the behavior of a more complex model, simulation, or real-world process, making it easier and faster to analyze or predict outcomes.
What is Surrogate Model?
A surrogate model learns to reproduce the input-output behavior of a complex system without directly running the original system each time. It is typically trained using data generated by the original model, simulation, or process and can then provide approximate results much more quickly.
Why is Surrogate Model Important?
Surrogate models can significantly reduce computational cost and execution time when the original system is expensive or slow to evaluate. They can also support experimentation, optimization, and analysis while providing a more accessible representation of complex systems.
Common use cases
Surrogate Models are commonly used in engineering simulations, scientific computing, hyperparameter optimization, risk analysis, digital twins, and machine learning model evaluation.