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In this section, we use interpolation in Matlab. We look at 1D and 2D interpolation and using linear or spline fits through our data points.
At the end of this section you should be able to:
Perform interpolation in 1D using different continuity conditions
Perform interpolation in 2D
1D Interpolation
1D Interpolation
1D Linear Interpolation: Estimate the dependent variable by using a weighted average between its two nearest measured locations.
1D Linear Interpolation Example: Using linear interpolation to find the enthalpy and entropy values of steam at a specified temperature not on the steam table.
1D Cubic Spline Interpolation: Higher order interpolation that may better fit the shape of the function generating the experimental data.
2D Interpolation
2D Interpolation
2D Interpolation: Estimating the value of a dependent function of two independent variables using linear or cubic spline interpolation.
Interpolation vs. Fitting
Interpolation vs. Fitting
Lecture Code
Lecture Code
Additional Resources
Additional Resources
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