🧭 Spherical Harmonics and the Spherical Coordinate System
Spherical geometry plays a fundamental role in physics, signal processing, and geometric deep learning.Spherical harmonics are one of the most important mathematical tools for analyzing functions defined on the surface of a sphere. They appear in many areas including quantum mechanics, geophysics, computer graphics, and modern geometric deep learning.
This post introduces the spherical coordinate system and explains how spherical harmonics arise naturally when studying functions on the sphere.
The Spherical Coordinate System
In three-dimensional space, we often represent points using Cartesian coordinates
\[ (x, y, z) \]However, when working with spherical objects or rotationally symmetric problems, it is more natural to use spherical coordinates:
\[ (r, \theta, \phi) \]where:
- \(r\) is the distance from the origin
- \(\theta\) is the polar angle (measured from the z-axis)
- \(\phi\) is the azimuthal angle (measured in the xy-plane)
The relationship between Cartesian and spherical coordinates is:
\[ x = r \sin\theta \cos\phi \]\[ y = r \sin\theta \sin\phi \]\[ z = r \cos\theta \]This coordinate system is particularly useful when studying functions defined on the surface of a sphere, where \(r\) is constant.
What Are Spherical Harmonics?
Spherical harmonics are a set of special functions defined on the sphere that form an orthogonal basis for functions on the sphere.
They are analogous to Fourier series, but instead of decomposing functions on a circle, they decompose functions on a sphere.
A spherical harmonic is usually written as
\[ Y_l^m(\theta, \phi) \]where:
- \(l\) is the degree
- \(m\) is the order
- \(-l \le m \le l\)
These functions arise as solutions to Laplace’s equation in spherical coordinates.
Why Spherical Harmonics Matter
Spherical harmonics appear in many scientific fields:
Physics
They are used to solve:
- Schrödinger equation
- gravitational potentials
- electromagnetic fields
Computer Graphics
They are used for:
- lighting models
- environment maps
- rendering reflections
Geometric Deep Learning
Modern AI models that operate on spheres, rotations, or 3D structures often rely on spherical harmonics for:
- equivariant neural networks
- molecular modeling
- 3D vision
Visualizing the Harmonics
Each spherical harmonic corresponds to a particular frequency pattern on the sphere.
Low-degree harmonics represent smooth variations, while higher-degree harmonics represent finer oscillations.
Conceptually:
