Modeling#
This section of the Jupyter Book covers concepts on different surrogate modeling methods and covers different methods of creating and testing surrogate models. The section starts by discussing sampling plans and data handling concepts which are crucial for data generation and preprocessing before the creation of surrogate models. Then, this section covers basic surrogate modeling methods, such as polynomial models, Gaussian process models, and neural networks. Finally, the section will cover advanced modeling methods such as multifidelity methods and multioutput surrogate modeling strategies. This section also covers methods such as cross validation, which can be used for hyperparameter selection for the surrogate models.
This section covers the following topics:
Sampling Plans
Data Handling
Basic Surrogate Models
Advanced Surrogate Models