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In mathematics, a tensor is a certain kind of geometrical entity which generalizes the concepts of scalar, vector and linear operator in a way that is independent of any chosen frame of reference. Tensors are of importance in differential geometry, physics and engineering. Einstein's theory of general relativity is formulated completely in the language of tensors.
Note that the word "tensor" is often used as a shorthand for tensor field, a concept which defines a tensor value at every point in a manifold. To understand tensor fields, you need to first understand tensors.
There are two ways of approaching the definition of tensors:
This article attempts to provide a nontechnical introduction to the idea of tensors, and to provide an introduction to the two articles which describe two different and complementary treatments of the theory of tensors in detail.
Some well known examples of tensors in geometry are quadratic forms, and the curvature tensor. Examples of physical tensors are the energymomentum tensor[?] and the polarization tensor[?].
Geometric and physical quantities may be categorized by considering the degrees of freedom inherent in their description. The scalar quantities are those that can be represented by a single number  speed, mass, temperature, for example. There are also vectorlike quantities, such as force, that require a list of numbers for their description. Finally, quantities such as quadratic forms naturally require a multiply indexed array for their representation. These latter quantities can only be conceived of as tensors.
Actually, the tensor notion is quite general, and applies to all of the above examples; scalars and vectors are special kinds of tensors. The feature that distinguishes a scalar from a vector, and distinguishes both of those from a more general tensor quantity is the number of indices in the representing array. This number is called the rank of a tensor. Thus, scalars are rank zero tensors (no indices at all), and vectors are rank one tensors.
As a simple example, consider a ship in the water. We want to describe its response to an applied force. Force is a vector, and the ship will respond with an acceleration, which is also a vector. The acceleration will in general not be in the same direction as the force, because of the particular shape of the ship's body. However, it turns out that the relationship between force and acceleration is linear. Such a relationship is described by a tensor of type (1,1) (that is to say, it transforms a vector into another vector). The tensor can be represented as a matrix which when multiplied by a vector results in another vector. Just as the numbers which represent a vector will change if one changes the coordinate system, the numbers in the matrix that represents the tensor will also change when the coordinate system is changed.
In engineering, the stresses inside a rigid body or fluid are also described by a tensor; the word "tensor" is Latin for something that stretches, i.e. causes tension. If a particular surface element inside the material is singled out, the material on one side of the surface will apply a force on the other side. In general, this force will not be orthogonal to the surface, but it will depend on the orientation of the surface in a linear manner. This is described by a tensor of type (2,0), or more precisely by a tensor field of type (2,0) since the stresses may change from point to point.
Not all relationships in nature are linear, but most are differentiable and so may be approximated with sums of multilinear maps. Thus most quantities in the physical sciences can be expressed as tensors.
There are two equivalent approaches to visualizing and working with tensors.
This idea can then be further generalized to tensor fields, where the elements of the tensor are functions, or even differentials. In other words, a tensor can roughly be viewed in this approach as an extension of the idea of a Jacobian.
This treatment has largely replaced the componentbased treatment for advanced study, similar to the way that the more modern componentfree treatment of vectors replaces the traditional componentbased treatment after the componentbased treatment has been used to provide an elementary motivation for the concept of a vector.
In the end the same computational content is expressed, both ways.
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