Basic Surrogate Models

This section provides implementation for the concepts covered in the class for basic surrogate models. As discussed in the lecture, one of the ways to create a model of \(y(x)\) is by considering a linearly weighted combination of basis functions. Mathematically, this can be represented as:

\[ y(x) \sim \hat{y}(x) = \mathbf{w}^T\pmb{\psi} = \sum w_i \psi_i(x) \]

where \(\mathbf{\pmb{\psi}}\) is the vector of basis functions and \(\mathbf{w}\) is the vector of weights. The model is linear in terms of weights \(w_i\) but basis function \(\psi_i(x)\) can be non-linear. For example, the basis functions vector \(\pmb{\psi}\) can be \([1 \text{ } x \text{ } x^2 \text{ } \sin(x) \text{ } e^{x}]^T\). The weights \(w_i\) are determined by minimizing the sum of squared differences between the predictions and actual values. The weights obtained after minimization are given by (refer lecture notes for derivation):

\[ \mathbf{w} = (\Psi^T\Psi)^{-1}\Psi^T\mathbf{y} = \Psi^{\dagger}\mathbf{y} \]

where \(\mathbf{y}\) is the vector of target values and \(\Psi^{\dagger}\) is the (Moore-Penrose) pseudo-inverse of \(\Psi\) matrix. The pseudo-inverse will be regular inverse if \(\Psi\) is invertible. The performance of the model can be evaluated using the mean squared error (MSE) which is given by:

\[ \text{RMSE} = \sqrt{ \frac{1}{N}\sum_{i=1}^{N} (y_i - \hat{y}_i)^2 } \]

where \(N\) is the number of data points, \(y_i\) and \(\hat{y}_i\) are the true and predicted values respectively.

In this section, following models are covered:

1. Linear model

2. Polynomial model

3. Radial basis function model