Interpolation of a rapidly oscillating function

In summary, "Interpolation of a rapidly oscillating function" discusses techniques used to approximate functions that exhibit rapid fluctuations. It highlights the challenges faced in accurately capturing these oscillations using traditional interpolation methods. The paper emphasizes the importance of choosing appropriate basis functions and interpolation points to enhance the accuracy of the representation. Various mathematical approaches and their effectiveness in maintaining fidelity to the original function are analyzed, offering insights into improving interpolation for complex, oscillatory behaviors.
  • #1
CAF123
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I have an analytic function F(x,y,z) and grids in x,y and z. I would like to reproduce the values I get for F at a given x,y and z through carefully interpolating the values given in the grids I have in each of these variables. The problem is that in some part of the x,y,z phase space, namely when y->0, the function F(x,y,z) oscillates very heavily and I am not sure how to tackle the interpolation.

My interpolation routine is in Cpp and, for a good part of my phase space, linear interpolation is OK but for y->0 I see it is not and was thinking of using splines. But, I see only a max 2D spline given in the gsl_library and I am anyway not sure if splines would help here when the function oscillates rapidly.

Thanks in advance for any comments.
 
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  • #2
What form does the oscillation take?
Map the axes through a function that corrects the period to be a constant.
Consider using a 3D Fourier transform as the interpolator.
 
  • #3
CAF123 said:
I am anyway not sure if splines would help here when the function oscillates rapidly.
Probably not, you can't extract more information than the grid can encode (oscillations at twice the interval of the grid points by the Nyquist-Shannon theorem).

Below this limit you could extract more accuracy using e.g. https://www.geometrictools.com/GTE/Mathematics/IntpTricubic3.h.
 
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  • #4
Is there some other properties of the function ##F(x,y,z)## that can be used in decision what interpolation would be the best for it
- It is analytical . Does it can be represented in the following form
$$F(x,y,z)=\sum_{n=0}^{\infty} a_n (x-x_0)^n+b_n(y-y_0)^n+c_n(z-z_0)^n$$
- It oscillates. What are features of those oscillations ? ( amplitude, frequency property )
- Why oscillates more when ##y \to 0## ?
Maybe combination of a trigonometric function and an interpolation can describe it better.
 

FAQ: Interpolation of a rapidly oscillating function

What is interpolation of a rapidly oscillating function?

Interpolation of a rapidly oscillating function involves estimating values of the function at certain points based on known values at other points. Rapid oscillations can make traditional interpolation methods, such as polynomial interpolation, less effective due to phenomena like Runge's phenomenon, where oscillations increase near the edges of the interpolation interval.

Why is interpolation of rapidly oscillating functions challenging?

The challenge arises because rapidly oscillating functions can change values significantly over small intervals, leading to large errors in estimation if a simple interpolation method is used. This requires more sophisticated techniques that can capture the oscillatory behavior without introducing large errors.

What methods are commonly used for interpolating rapidly oscillating functions?

Common methods include spline interpolation, particularly cubic splines, which can provide smoother approximations. Additionally, Fourier series and wavelet transforms are often used, as they can effectively represent oscillatory functions and allow for better handling of high-frequency components.

How does the choice of interpolation points affect the accuracy?

The choice of interpolation points is crucial; strategically selecting points, such as using Chebyshev nodes, can minimize interpolation errors. Spacing points closer together in regions of rapid oscillation can improve accuracy, while maintaining a balance to avoid excessive computational complexity.

Are there specific applications for interpolating rapidly oscillating functions?

Yes, applications include signal processing, where rapidly oscillating signals are common, and in numerical solutions of differential equations, particularly in physics and engineering where wave-like phenomena occur. Accurate interpolation is vital for simulations and modeling in these fields.

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