# Interpolation

The Interpolation package in this repository provides tools and algorithms for estimating unknown values within the range of a discrete set of known data points. It includes functionalities for various interpolation methods, making it a comprehensive solution for data analysis and modeling tasks.

## Radial Basis Function (RBF) Model

The [`Radial Basis Function (RBF)`](rbf_interpolation.md) model is a powerful interpolation method that uses radial basis functions to approximate the underlying function of the data. RBF interpolation is particularly useful for multidimensional data and can handle irregularly spaced data points.

### Key Features of the RBF Model

- **Flexibility**: RBF interpolation can be applied to data in any number of dimensions.
- **Smoothness**: The resulting interpolated surface is smooth and continuous.
- **Versatility**: Various types of radial basis functions (e.g., Gaussian, Multiquadric, Inverse Multiquadric) can be used to tailor the interpolation to specific needs.
- **Scalability**: Suitable for large datasets with efficient computation methods.

### Applications

- **Geospatial Analysis**: Interpolating spatial data such as elevation, temperature, or precipitation.
- **Engineering**: Modeling physical phenomena like stress, strain, or fluid dynamics.
- **Machine Learning**: Enhancing feature spaces and improving model accuracy through smooth approximations.

The RBF model in this package provides a robust and versatile tool for interpolation tasks, ensuring accurate and reliable results for a wide range of applications.
