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Gauss's Law

Gauss's Law

If a closed surface does not have any charge inside where an electric field line can terminate, then the electric field line entering the surface at one point must necessarily exit at some other point of the surface. Therefore, if a closed surface does not have any charges inside the enclosed volume, then the electric flux through the surface is zero. What happens to the electric flux if there are some charges inside the enclosed volume? Gauss's law gives a quantitative answer to this question.

Gauss's Law: Spherical Symmetry

Gauss's Law: Spherical Symmetry

A charge distribution has spherical symmetry if the density of charge depends only on the distance from a point in space and not on the direction. In other words, if the system is rotated, it doesn't look different. For instance, if a sphere of radius R is uniformly charged with charge density ρ0, then the distribution has spherical symmetry. On the other hand, if a sphere of radius R is charged so that the top half of the sphere has a uniform charge density ρ1 and the bottom half has a uniform...

Gauss's Law: Cylindrical Symmetry

Gauss's Law: Cylindrical Symmetry

A charge distribution has cylindrical symmetry if the charge density depends only upon the distance from the axis of the cylinder and does not vary along the axis or with the direction about the axis. In other words, if a system varies if it is rotated around the axis or shifted along the axis, it does not have cylindrical symmetry. In real systems, we do not have infinite cylinders; however, if the cylindrical object is considerably longer than the radius from it that we are interested in,...

Gauss's Law: Problem-Solving

Gauss's Law: Problem-Solving

Gauss's law helps determine electric fields even though the law is not directly about electric fields but electric flux. In situations with certain symmetries (spherical, cylindrical, or planar) in the charge distribution, the electric field can be deduced based on the knowledge of the electric flux. In these systems, we can find a Gaussian surface S over which the electric field has a constant magnitude. Furthermore, suppose the electric field is parallel (or antiparallel) to the area vector...

Gauss's Law: Planar Symmetry

Gauss's Law: Planar Symmetry

A planar symmetry of charge density is obtained when charges are uniformly spread over a large flat surface. In planar symmetry, all points in a plane parallel to the plane of charge are identical with respect to the charges. Suppose the plane of the charge distribution is the xy-plane, and the electric field at a space point P with coordinates (x, y, z) is to be determined. Since the charge density is the same at all (x, y) - coordinates in the z = 0 plane, by symmetry, the electric field at P...

Gaussian Elimination: Problem Solving

Gaussian Elimination: Problem Solving

Systems of linear equations in several variables are pivotal in modeling complex scenarios involving multiple unknowns and constraints. Such systems are widely used in various fields to represent relationships where several conditions must be simultaneously satisfied. Each variable in the system corresponds to an unknown quantity, while each equation imposes a linear constraint, leading to a structured approach for analyzing and solving real-world problems.A system of three equations with three...

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Related Experiment Video

Updated: Jul 1, 2026

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry

Published on: August 12, 2013

The variational gaussian approximation revisited.

Manfred Opper¹, Cédric Archambeau

¹Department of Computer Science, Technical University Berlin, D-10587 Berlin, Germany. opperm@cs.tu-berlin.de

Neural Computation

|September 13, 2008

Summary

This summary is machine-generated.

This study shows that Gaussian variational approximation, often computationally expensive, can be efficient for specific models. By connecting Laplace and variational approximations, the number of parameters is reduced to linear complexity.

Related Experiment Videos

Last Updated: Jul 1, 2026

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry

The Generation of Higher-order Laguerre-Gauss Optical Beams for High-precision Interferometry

Published on: August 12, 2013

Area of Science:

Machine Learning
Statistical Inference
Probabilistic Modeling

Background:

Variational approximation is key for approximating posterior distributions in machine learning.
Multivariate Gaussian approximations are less favored than factorizing distributions due to high computational complexity (O(N^2) parameters).

Purpose of the Study:

To investigate the relationship between Laplace and variational approximations.
To demonstrate a method for reducing the number of variational parameters in Gaussian approximations.

Main Methods:

Analyzing the connection between Laplace and variational inference.
Developing an approach for models with Gaussian priors and factorizing likelihoods.

Main Results:

The number of variational parameters for Gaussian approximation is reduced to O(N) for specific model classes.
This optimized approach is applicable to Gaussian process regression with non-Gaussian likelihoods.

Conclusions:

Gaussian variational approximation can be computationally efficient under certain conditions.
The proposed method offers a practical alternative for complex probabilistic models.