# 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 and radial basis function models. Finally, the section will cover advanced modeling methods such as Gaussian process models, neural networks, 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:
1. Sampling plans
2. Data handling
3. Basic surrogate models
4. Gaussian process Models
5. Neural networks
6. Multifidelity neural networks
7. Proper orthogonal decomposition
8. Autoencoder networks
9. Multioutput models