What is a covariant derivative

In summary, a covariant derivative is a coordinate-dependent adjustment to the ordinary derivative that ensures that each partial derivative of each coordinate unit vector is zero. This adjustment is made by a linear operator known as the connection or the Christoffel symbol. The covariant derivative of the metric is zero, and unlike the ordinary derivative, the covariant derivative of any tensor is also a tensor with an extra covariant index. A vector is said to be parallel transported along a curve if its covariant directional derivative in the direction of the curve's tangent is zero. The equations for this derivative involve the Christoffel symbol and the metric. The Christoffel symbol is symmetric in its lower indices and can be expressed in terms of the metric. The parallel
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Definition/Summary

Covariant derivative, [itex]D[/itex], is a coordinate-dependent adjustment to ordinary derivative which makes each partial derivative of each coordinate unit vector zero: [itex]D\hat{\mathbf{e}}_i/\partial x_j\ =\ 0[/itex]

The adjustment is made by a linear operator known both as the connection, [itex]\Gamma^i_{\ jk}[/itex], and as the Christoffel symbol, [itex]\{^{\ i\ }_{j\ k}\}[/itex].

Covariant derivative of the metric ([itex]g_{ij}[/itex]) is zero.

Covariant derivative (unlike ordinary derivative) of any tensor is also a tensor (with an extra covariant index).

A vector [itex]V^i[/itex] is parallel transported along a curve [itex]x^i(s)[/itex] with tangent [itex]T^i(s)[/itex] if its covariant directional derivative in the direction of that tangent is zero: [itex](T.D)V^i/ds\ =\ 0[/itex]

Equations

[tex]\frac{DV^i}{\partial x^j}\left(\text{also written}\ \frac{DV^i}{Dx^j}\ \text{or}\ \nabla_jV^i\ \text{or}\ V^i_{\ ;\,j}\right)\ =\ \frac{\partial V^i}{\partial x^j}\ -\ \Gamma^i_{\ jk}V^k[/tex]

[tex]\frac{D\,(a^i\hat{\mathbf{e}}_i)}{\partial x^j}\ =\ 0\ \text{for constant}\ a^i[/tex]

[tex]\frac{Dg^{ij}}{\partial x^k}\ =\ 0[/tex]

[tex]\Gamma^i_{\ jk}\ =\ \Gamma^i_{\ kj}[/tex]

[tex]\Gamma^i_{\ jk}\ =\ \{^{\ i\ }_{j\ k}\}\ =\ \frac{1}{2}\,g^{il}\left(\frac{\partial g_{jl}}{\partial x^k}\ +\ \frac{\partial g_{kl}}{\partial x^j}\ -\ \frac{\partial g_{jk}}{\partial x^l}\right)[/tex]

Parallel transport of a vector [itex]V^i[/itex] along a curve [itex]x^i(s)[/itex]:

[tex]\frac{dx^j}{ds}\frac{DV^i}{\partial x^j}\ =\ 0\ \ \text{or}\ \ dV^i = -\Gamma^i_{\ jk}V^kdx^j[/tex]

Geodesic deviation equation:

[tex]\frac{D^2\,\delta x^{\alpha}}{d\tau^2}\ =\ -\,R^{\alpha}_{\ \mu\beta\sigma}\,V^{\mu}\,V^{\sigma}\,\delta x^{\beta}[/tex]

Extended explanation



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Related to What is a covariant derivative

What is a covariant derivative?

A covariant derivative is a mathematical concept in differential geometry that extends the concept of a derivative to curved spaces. It is a way of measuring how a vector field changes as it moves along a given path on a curved surface.

How is a covariant derivative different from a regular derivative?

A regular derivative measures how a function changes with respect to its input variables, while a covariant derivative measures how a vector field changes as it moves along a curved surface. In other words, a covariant derivative takes into account the curvature of the space, while a regular derivative does not.

What is the purpose of a covariant derivative?

The purpose of a covariant derivative is to extend the concept of a derivative to curved spaces, where the usual rules of differentiation do not apply. It allows us to define concepts such as tangent vectors, geodesics, and curvature on curved surfaces.

How is a covariant derivative calculated?

A covariant derivative is calculated using a connection, which is a geometric object that describes how vectors change as they move along a curved surface. The connection is used to define a covariant derivative operator, which acts on vector fields to produce another vector field.

What are some real-world applications of covariant derivatives?

Covariant derivatives have many applications in physics and engineering, such as in general relativity, fluid dynamics, and electromagnetism. They are also used in computer graphics and computer vision to describe the motion of objects in 3D space.

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