Orthogonal 3D Basis Functions in Spherical Coordinates

In summary, the conversation discusses the best way to expand a 3D scalar function in an orthogonal spherical 3D basis set. Different options are considered, such as using spherical harmonics or the normalized hydrogenic radial wavefunctions. The goal is to find a functional family that satisfies orthogonality and can accurately represent the desired function. Various approaches, such as using the sinc function or fitting an expansion, are discussed. A resource on Fourier analysis in polar and spherical coordinates is also recommended.
  • #1
dreens
40
11
I'd like to expand a 3D scalar function I'm working with, ##f(r,\theta,\phi)##, in an orthogonal spherical 3D basis set. For the angular component I intend to use spherical harmonics, but what should I do for the radial direction?

Close to zero, ##f(r)\propto r##, and above a fuzzy threshold ##r_0## I don't really care about the value of f anymore. So I can make it go to zero exponentially, polynomially, or just cut it off suddenly depending on which allows me to have the best numerical computation time for the expansion I obtain.

It seems like I have a number of options, like the normalized hydrogenic radial wavefunctions since they're automatically orthonormal by virtue of being the solution of schrodinger's equation. This seems silly though since my scalar has nothing to do with hydrogen or schrodinger's equation.

Maybe the spherical bessel functions, but they don't seem to converge when I integrate them, since they go to zero to slowly.

I suppose I could just do a Fourier expansion with a cutoff at ##r_0## and just divide the basis functions by r so that when I do the spherical integral the ##r^2## in the Jacobean gives me the usual Fourier orthogonality. Anything wrong with this way?
 
  • #3
Okay, I have an update and I'll try and add some more details and pretty equations :-).

So I'd like to represent ##h(r,\phi,\theta)## as an expansion of orthonormal functions:

$$h(r,\theta,\phi) = \sum_{n=0}^\infty \sum_{l=0}^\infty \sum_{m=-l}^l A_{nlm} f_n(r)g_{lm}(\theta,\phi)\\$$

Where by orthonormality I mean that the families ##g_{lm}## and ##f_n## satisfy the following:
$$\int_0^\pi\sin(\theta) d\theta\int_0^{2\pi}d\phi\,g_{lm}(\theta,\phi)g_{l^*m^*}(\theta,\phi)=\delta_{ll^*}\delta_{mm^*}\\
\int_0^{r_0}r^2dr f_n(r)f_{n^*}(r)=\delta_{nn^*}$$

Which would allow me to calculate each ##A_{nlm}## by convolving h with the corresponding members of the orthonormal families:

$$A_{nlm} = \int_0^{r_0}r^2dr\int_0^\pi\sin(\theta) d\theta\int_0^{2\pi}d\phi\,h(r,\theta,\phi)\cdot f_n(r) g_{lm}(\theta,\phi)$$

The reason I want to do this is that I only have access to a numerical approximation of ##h## evaluated on a grid of data points, but I'd like to perform a routine that would involve calculating symbolic second order derivatives of ##h##, which I could do if I first represent ##h## as an expansion of differentiable analytic functions.

For ##g_{lm}## I am using the well known spherical harmonics, with a slight modification to focus on my real rather than complex scalar:

$$g_{lm} = N_{lm} P_l^m(\cos(\theta))\text{sincos}(m,\phi)\\
\text{sincos}(m,x) = \begin{cases}
\cos(x) & m<0 \\
1& m=0 \\
\sin(x)& m>0
\end{cases}\\
N_{lm}\text{ defined s.t. }\int_0^\pi\sin(\theta) d\theta\int_0^{2\pi}d\phi \left(N_{lm}P_l^m(\cos(\theta))\text{sincos}(m,\phi)\right)^2 = 1
$$

In my first post above, I mentioned my intention to try using ##f_n(r) = \sin(n\pi r/r_0)/r##: We can check orthogonality:

$$ \int_0^{r_0}r^2dr\,\sin(n\pi r/r_0)/r\sin(m\pi r/r_0)/r = N_n^2\delta_{nm} $$

The ##r^2## cancels the two ##1/r##'s, leaving the usual Fourier orthogonality, and we can make the related orthonormal set ##\tilde{f}_n=f_n/N_n##.

Unfortunately this fails to converge to my desired function ##h##, because I know that close to ##r=0##, ##h(r)\propto r##, whereas the sinc function ##\sin(r)/r\propto1##. If I could take a large number of terms, it would do better and better, but I would be better off choosing a family of functions ##f_n(r)## such that for all ##n##, ##f_n(r)\propto r## near ##r=0##. Does anyone know such a family that also satisfies the requisite orthogonality?

I also tried the family of Hydrogen wavefunctions, since at least for l=1, their radial components go to ##r=0## like ##r##. The Laguerre and Legendre polynomials proved a bit too computationally intensive for my dataset. I may still be able to get it to work, but I'd rather hunt for a better functional family.

Another idea I had was to just fit an expansion to ##h(r) - \alpha r##, where I just subtract away the linear behavior close to ##r=0## and then use the sinc family to fit the rest. This would still give me the analytic expression for ##h## I desire, even though one of the terms wouldn't truly belong to my chosen functional family. I'm stalled on this approach however, because actually there's angle dependence: ##h(r) = \alpha(\theta,\phi)\cdot r## as ##r\rightarrow 0##. I suppose I could first fit alpha with a 2D spherical expansion and go from there. But again, it would be nice to find the right family of functions and do a single complete 3D expansion.
 
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Likes S.G. Janssens
  • #4
Thank you, I read bits and pieces (time permitting), but I wanted to let you know that I appreciate your follow-up.
 

Related to Orthogonal 3D Basis Functions in Spherical Coordinates

1. What are orthogonal 3D basis functions in spherical coordinates?

Orthogonal 3D basis functions in spherical coordinates are a set of mathematical functions used to describe three-dimensional (3D) space in terms of spherical coordinates, which include radial distance, azimuthal angle, and polar angle. These functions are used to represent the shape and orientation of objects in 3D space.

2. How are orthogonal 3D basis functions in spherical coordinates different from other basis functions?

Orthogonal 3D basis functions in spherical coordinates are specifically designed to be independent of each other, meaning they do not overlap or affect each other's values. This is in contrast to other basis functions, such as Cartesian coordinates, where the functions are not completely independent and can have overlapping values.

3. What is the significance of using orthogonal 3D basis functions in spherical coordinates?

Using orthogonal 3D basis functions in spherical coordinates allows for a more efficient and accurate representation of 3D objects, as these functions are able to capture the unique shape and orientation of an object without interference from other basis functions. This makes them especially useful for applications in physics, chemistry, and engineering.

4. How are orthogonal 3D basis functions in spherical coordinates calculated?

Orthogonal 3D basis functions in spherical coordinates are calculated using mathematical expressions that depend on the values of the radial distance, azimuthal angle, and polar angle. These expressions are derived from the spherical harmonic functions, which are used to describe spherical symmetry in quantum mechanics.

5. What are some practical applications of orthogonal 3D basis functions in spherical coordinates?

Orthogonal 3D basis functions in spherical coordinates have many practical applications, such as in computer graphics, molecular modeling, and signal processing. They are also commonly used in physics simulations and in the analysis of data from remote sensing technologies, such as satellite imagery.

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