Tensor
From Academic Kids

In mathematics, a tensor is a certain kind of geometrical entity, or alternatively generalized 'quantity'. The tensor concept includes the ideas of scalar, vector and linear operator. Tensors may be written down in terms of coordinate systems, as arrays of scalars, but are defined so as to be independent of any chosen frame of reference. Tensors are of importance in physics and engineering. In the field of diffusion tensor imaging, for instance, a tensor quantity that expresses the differential permeability of organs to water in varying directions is used to produce scans, for example of the brain.
While tensors can be represented by multidimensional arrays of components, the point of having a tensor theory is to explain further implications of saying that a quantity is a tensor, beyond that specifying it requires a number of indexed components. In particular, tensors behave in specific ways under coordinate transformations. The abstract theory of tensors is a branch of linear algebra, now called multilinear algebra.
This article attempts to provide a nontechnical introduction to the idea of tensors, and to provide an introduction to the articles which describe different, complementary treatments of the theory of tensors in detail.
Contents 
Background
The word "tensor" was first introduced by William Rowan Hamilton in 1846, but he used the word for what is now called modulus. The word was used in its current meaning by Woldemar Voigt in 1899.
The notation was developed around 1890 by Gregorio RicciCurbastro under the title absolute differential geometry, and made accessible to many mathematicians by the publication of Tullio LeviCivita's classic text The Absolute Differential Calculus in 1900 (in Italian; translations followed). The tensor calculus achieved broader acceptance with the introduction of Einstein's theory of general relativity, around 1915. General Relativity is formulated completely in the language of tensors, which Einstein had learned from LeviCivita himself with great difficulty. But tensors are used also within other fields such as continuum mechanics, for example the strain tensor, (see linear elasticity).
Note that the word "tensor" is often used as a shorthand for tensor field, which is a tensor value defined at every point in a manifold. To understand tensor fields, you need to first understand the basic idea of tensors.
The choice of approach
There are two ways of approaching the definition of tensors:
 The usual physics way of defining tensors, in terms of objects whose components transform according to certain rules, introducing the ideas of covariant or contravariant transformations.
 The usual mathematics way, which involves defining certain vector spaces and not fixing any coordinate systems until bases are introduced when needed. Covariant vectors, for instance, can also be described as oneforms, or as the elements of the dual space to the contravariant vectors.
(Of course, physicists and engineers are among the first to recognise that vectors and tensors have a physical significance as entities, which goes beyond the (often arbitrary) coordinate system in which their components are enumerated. Similarly, mathematicians find there are some tensor relations which are more conveniently derived in a coordinate notation).
Examples
Not all relationships in nature are linear, but most are differentiable and so may be locally approximated with sums of multilinear maps. Thus most quantities in the physical sciences can be usefully expressed as 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.
Some well known examples of tensors in geometry are quadratic forms, and the curvature tensor. Examples of physical tensors are the energymomentum tensor, the inertia 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.
Another example of a tensor is the Riemann curvature tensor which is in 4th order with 4 dimensions (3 spatial + time = 4 dimensions). Since it is in 4th order with 4 dimensions, a 256 member matrix evolves. Only 20 of these components are actually independent of each other, greatly simplifying the matrix.
Approaches, in detail
There are equivalent approaches to visualizing and working with tensors; that the content is actually the same may only become apparent with some familiarity with the material.
 The classical approach views tensors as multidimensional arrays that are ndimensional generalizations of scalars, 1dimensional vectors and 2dimensional matrices. The "components" of the tensor are the indices of the array. This idea can then be further generalized to tensor fields, where the elements of the tensor are functions, or even differentials.
 The tensor field theory can roughly be viewed, in this approach, as a further extension of the idea of the Jacobian.
 The modern approach
 The modern (componentfree) approach views tensors initially as abstract objects, expressing some definite type of multilinear concept. Their wellknown properties can be derived from their definitions, as linear maps or more generally; and the rules for manipulations of tensors arise as an extension of linear algebra to multilinear algebra. This treatment has largely replaced the componentbased treatment for advanced study, in 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. You could say that the slogan is 'tensors are elements of some tensor space'.
 The intermediate treatment of tensors article attempts to bridge the two extremes, and to show their relationships.
In the end the same computational content is expressed, both ways. See glossary of tensor theory for a listing of technical terms.
Tensor densities
It is also possible for a tensor field to have a "density". A tensor with density r transforms as an ordinary tensor under coordinate transformations, except that it is also multiplied by the determinant of the Jacobian to the r^{th} power. This is best explained, perhaps, using vector bundles: where the determinant bundle of the tangent bundle is a line bundle that can be used to 'twist' other bundles r times.
Tensor rank
See Tensor Standard FormRank  Alias  Element notation  Common transformation^{*} 

0  Scalar  a  S'=aS 
1  Vector  a_{i}  V'_{i}=aa_{ij}V_{j} 
2  Matrix  a_{i}_{j}  M'_{ij}=aa_{ik}a_{jl}M_{kl} 
3  rank3 tensor  a_{ijk}  M'_{ijk}=aa_{il}a_{js}a_{km}M_{lsm} 
^{*} a is the determinant of the coefficient array a_{mn} or its corresponding in the given dimension. Note that quantities that transform according to column 4 are usually called tensor densities.
See also
Notation
Foundational
Applications
 tensor derivative
 absolute differentiation
 curvature
 Riemannian geometry
 Application of tensor theory in engineering science
External links
 A discussion of the various approaches to teaching tensors, and recommendations of textbooks (http://nrich.maths.org/askedNRICH/edited/2604.html)
 A thread discussing basic and in depth definitions as well as various examples (http://www.physicsforums.com/showthread.php?t=35920&page=1&pp=15)
 Light Cone dynamics (http://www.lightcones.blogspot.com)
 Introduction to Tensor Calculus and Continuum Mechanics (http://www.math.odu.edu/~jhh/counter2.html)
Reference books
 Tensors, Differential Forms, and Variational Principles (1989) David Lovelock, Hanno Rund
 Tensor Analysis on Manifolds (1981) Richard L Bishop, Samuel I. Goldberg
 Introduction to Tensor Calculus, Relativity and Cosmology (2003) D. F. Lawden
 Tensor Analysis (2003) L.P. Lebedev, Michael J. Cloud
 Calculus of Variations (2000) S. V. Fomin, I. M. Gelfand
Tensor software
 GRTensorII (http://grtensor.phy.queensu.ca/) is a computer algebra package for performing calculations in the general area of differential geometry. GRTensor II is not a stand alone package, the program runs with all versions of Maple V Release 3 through Maple 9.5. A limited version (GRTensorM) has been ported to Mathematica.
 MathTensor (http://smc.vnet.net/mathtensor.html) is a tensor analysis system written for the Mathematica system. It provides more than 250 functions and objects for elementary and advanced users.
 maxima (http://maxima.sourceforge.net/) is a GPL computer algebra system Template:Free software which should be usable for making tensor algebra calculations
 tensors in maxima (http://maxima.sourceforge.net/docs/original/maxima_28.html#SEC89)
 Ricci (http://www.math.washington.edu/~lee/Ricci/) is a system for Mathematica 2.x and later for doing basic tensor analysis, available for free.de:Tensor
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