Surrogatmodells

Surrogatmodells, a term originating from German, translates to "surrogate model" in English. It refers to a simplified, computationally inexpensive mathematical model that approximates the behavior of a more complex, often computationally intensive, system. These models are employed when direct simulation or analysis of the original system is too time-consuming or resource-prohibitive.

The core idea behind a surrogate model is to capture the essential input-output relationship of the original system using a reduced number of calculations. This is typically achieved by sampling the original system's behavior at a limited number of points and then fitting a simpler function to these data points. Common techniques for building surrogate models include polynomial regression, Kriging (Gaussian process regression), radial basis functions, and artificial neural networks.

Surrogate models find widespread application across various engineering and scientific disciplines. In structural analysis, they can predict the response of a structure to different loads without running full finite element simulations. In fluid dynamics, they can approximate complex flow patterns. They are also crucial in optimization processes, allowing for rapid exploration of design spaces, and in uncertainty quantification, where repeated evaluations of a complex model are required. The accuracy of a surrogate model is dependent on the quality and quantity of the training data and the chosen approximation technique.