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{{about|tensors on a single vector space|vector fields|vector field|tensor fields|Tensor field|other uses|Tensor (disambiguation)}}{{short description|Algebraic object with geometric applications}}File:Components stress tensor.svg|right|thumb|300px|The second-order (Cauchy stress tensor]] in the basis (e1, e2, e3): mathbf{T} = begin{bmatrix}mathbf{T}^{(mathbf{e}_1)} mathbf{T}^{(mathbf{e}_2)} mathbf{T}^{(mathbf{e}_3)} end{bmatrix}, ormathbf{T} = begin{bmatrix} sigma_{11} & sigma_{12} & sigma_{13} sigma_{21} & sigma_{22} & sigma_{23} sigma_{31} & sigma_{32} & sigma_{33} end{bmatrix}.The columns are the respective stress vectors that act in the center of the cube, regarding to planes orthogonal to e1, e2, and e3. When mathbf{v} is given in this basis, the product of the two tensors, mathbf{T} cdot mathbf{v}, performed as matrix multiplication, yields the stress vector mathbf{T}^{(mathbf{v)}} in that point, which has its shear part in the plane orthogonal to mathbf{v}.)In mathematics, a tensor is an algebraic object that describes a linear mapping from one set of algebraic objects to another. Objects that tensors may map between include, but are not limited to vectors and scalars, and, recursively, even other tensors (for example, a matrix is a map between vectors, and is thus a tensor. Therefore a linear map between matrices is also a tensor). Tensors are inherently related to vector spaces and their dual spaces, and can take several different forms â€“ for example: a scalar, a tangent vector at a point, a cotangent vector (dual vector) at a point, or a multi-linear map between vector spaces. Euclidean vectors and scalars (which are often used in elementary physics and engineering applications where general relativity is irrelevant) are the simplest tensors.WEB
, What is a Tensor?
, Dissemination of IT for the Promotion of Materials Science
, University of Cambridge
, While tensors are defined independent of any basis, the literature on physics often refers to them by their components in a basis related to a particular coordinate system.An elementary example of mapping, describable as a tensor, is the dot product, which maps two vectors to a scalar. A more complex example is the Cauchy stress tensor T, which takes a directional unit vector v as input and maps it to the stress vector T(v), which is the force (per unit area) exerted by material on the negative side of the plane orthogonal to v against the material on the positive side of the plane, thus expressing a relationship between these two vectors, shown in the figure (right). The cross product, where two vectors are mapped to a third one, is strictly speaking not a tensor, because it changes its sign under those transformations that change the orientation of the coordinate system. The totally anti-symmetric symbol varepsilon_{ijk} nevertheless allows a convenient handling of the cross product in equally oriented three dimensional coordinate systems.Assuming a basis of a real vector space, e.g., a coordinate frame in the ambient space, a tensor can be represented as an organized multidimensional array of numerical values with respect to this specific basis. Changing the basis transforms the values in the array in a characteristic way that allows to define tensors as objects adhering to this transformational behavior. For example, there are invariants of tensors that must be preserved under any change of the basis, thereby making only certain multidimensional arrays of numbers a tensor. Compare this to the array representing varepsilon_{ijk} not being a tensor, for the sign change under transformations changing the orientation.Because the components of vectors and their duals transform differently under the change of their dual bases, there is a covariant and/or contravariant transformation law that relates the arrays, which represent the tensor with respect to one basis and that with respect to the other one. The numbers of, respectively, {{nowrap|vectors: {{mvar|n}}}} (contravariant indices) and dual {{nowrap|vectors: {{mvar|m}}}} (covariant indices) in the input and output of a tensor determine the type (or valence) of the tensor, a pair of natural numbers {{nowrap|{{math|(n, m)}}}}, which determine the precise form of the transformation law. The {{vanchor|order}} of a tensor is the sum of these two numbers.The order (also degree or {{vanchor|rank}}) of a tensor is thus the sum of the orders of its arguments plus the order of the resulting tensor. This is also the dimensionality of the array of numbers needed to represent the tensor with respect to a specific basis, or equivalently, the number of indices needed to label each component in that array. For example, in a fixed basis, a standard linear map that maps a vector to a vector, is represented by a matrix (a 2-dimensional array), and therefore is a 2nd-order tensor. A simple vector can be represented as a 1-dimensional array, and is therefore a 1st-order tensor. Scalars are simple numbers and are thus 0th-order tensors. This way the tensor representing the scalar product, taking two vectors and resulting in a scalar has order {{math|2 + 0 {{=}} 2}}, equal to the stress tensor, taking one vector and returning another {{math|1 + 1 {{=}} 2}}. The {{nowrap| varepsilon_{ijk}-symbol,}} mapping two vectors to one vector, would have order {{math|2 + 1 {{=}} 3.}}The collection of tensors on a vector space and its dual forms a tensor algebra, which allows products of arbitrary tensors. Simple applications of tensors of order {{math|2}}, which can be represented as a square matrix, can be solved by clever arrangement of transposed vectors and by applying the rules of matrix multiplication, but the tensor product should not be confused with this.Tensors are important in physics because they provide a concise mathematical framework for formulating and solving physics problems in areas such as mechanics (stress, elasticity, fluid mechanics, moment of inertia, ...), electrodynamics (electromagnetic tensor, Maxwell tensor, permittivity, magnetic susceptibility, ...), or general relativity (stressâ€“energy tensor, curvature tensor, ... ) and others. In applications, it is common to study situations in which a different tensor can occur at each point of an object; for example the stress within an object may vary from one location to another. This leads to the concept of a tensor field. In some areas, tensor fields are so ubiquitous that they are simply called "tensors".Tensors were conceived in 1900 by Tullio Levi-Civita and Gregorio Ricci-Curbastro, who continued the earlier work of Bernhard Riemann and Elwin Bruno Christoffel and others, as part of the absolute differential calculus. The concept enabled an alternative formulation of the intrinsic differential geometry of a manifold in the form of the Riemann curvature tensor.BOOK, Morris, Kline, Mathematical Thought From Ancient to Modern Times: Volume 3, {{google books, y, -OsRDAAAQBAJ, |date=March 1990|publisher=Oxford University Press, USA|isbn=978-0-19-506137-6}}

Definition

Although seemingly different, the various approaches to defining tensors describe the same geometric concept using different language and at different levels of abstraction.

As multidimensional arrays

A tensor may be represented as a (potentially multidimensional) array (although a multidimensional array is not necessarily a representation of a tensor, as discussed below with regard to holors). Just as a vector in an {{mvar|n}}-dimensional space is represented by a one-dimensional array of length {{mvar|n}} with respect to a given basis, any tensor with respect to a basis is represented by a multidimensional array. For example, a linear operator is represented in a basis as a two-dimensional square {{math|n Ã— n}} array. The numbers in the multidimensional array are known as the scalar components of the tensor or simply its components. They are denoted by indices giving their position in the array, as subscripts and superscripts, following the symbolic name of the tensor. For example, the components of an order {{math|2}} tensor {{mvar|T}} could be denoted {{math|T'ij}}â€¯, where {{mvar|i}} and {{mvar|j}} are indices running from {{math|1}} to {{mvar|n}}, or also by {{math|T {{su|b=j|p=i}}}}. Whether an index is displayed as a superscript or subscript depends on the transformation properties of the tensor, described below. Thus while {{math|T'ij}} and {{math|T {{su|b=j|p=i}}}} can both be expressed as n by n matrices, and are numerically related via index juggling, the difference in their transformation laws indicates it would be improper to add them together. The total number of indices required to identify each component uniquely is equal to the dimension of the array, and is called the order, degree or rank of the tensor. However, the term "rank" generally has another meaning in the context of matrices and tensors.Just as the components of a vector change when we change the basis of the vector space, the components of a tensor also change under such a transformation. Each type of tensor comes equipped with a transformation law that details how the components of the tensor respond to a change of basis. The components of a vector can respond in two distinct ways to a change of basis (see covariance and contravariance of vectors), where the new basis vectors mathbf{hat{e}}_i are expressed in terms of the old basis vectors mathbf{e}_j as,
mathbf{hat{e}}_i = sum_{j=1}^n mathbf{e}_j R^j_i = mathbf{e}_j R^j_i .
Here R' j'i are the entries of the change of basis matrix, and in the rightmost expression the summation sign was suppressed: this is the Einstein summation convention, which will be used throughout this article.The Einstein summation convention, in brief, requires the sum to be taken over all values of the index whenever the same symbol appears as a subscript and superscript in the same term. For example, under this convention B_iC^i = B_1C^1 + B_2C^2 + cdots B_nC^n The components v'i of a column vector v' transform with the inverse of the matrix R'',
hat{v}^i = left(R^{-1}right)^i_j v^j,
where the hat denotes the components in the new basis. This is called a contravariant transformation law, because the vector transforms by the inverse of the change of basis. In contrast, the components, w'i, of a covector (or row vector), w' transform with the matrix R'' itself,
hat{w}_i = w_j R^j_i .
This is called a covariant transformation law, because the covector transforms by the same matrix as the change of basis matrix. The components of a more general tensor transform by some combination of covariant and contravariant transformations, with one transformation law for each index. If the transformation matrix of an index is the inverse matrix of the basis transformation, then the index is called contravariant and is conventionally denoted with an upper index (superscript). If the transformation matrix of an index is the basis transformation itself, then the index is called covariant and is denoted with a lower index (subscript).As a simple example, the matrix of a linear operator with respect to a basis is a rectangular array T that transforms under a change of basis matrix R = left(R^j_iright) by hat{T} = R^{-1}TR. For the individual matrix entries, this transformation law has the form hat{T}^{i'}_{j'} = left(R^{-1}right)^{i'}_i T^i_j R^j_{j'} so the tensor corresponding to the matrix of a linear operator has one covariant and one contravariant index: it is of type (1,1).Combinations of covariant and contravariant components with the same index allow us to express geometric invariants. For example, the fact that a vector is the same object in different coordinate systems can be captured by the following equations, using the formulas defined above:
mathbf{v}=hat{v}^i ,mathbf{hat{e}}_i = left( left(R^{-1}right)^i_j {v}^j right) left( mathbf{{e}}_k R^k_i right) =left( left(R^{-1}right)^i_j R^k_i right) {v}^j mathbf{{e}}_k = delta_j^k {v}^j mathbf{{e}}_k ={v}^k ,mathbf{{e}}_k =
{v}^i ,mathbf{{e}}_i,
where delta^k_j is the Kronecker delta, which functions similarly to the identity matrix, and has the effect of renaming indices (j into k in this example). This shows several features of the component notation: the ability to re-arrange terms at will (commutativity), the need to use different indices when working with multiple objects in the same expression, the ability to rename indices, and the manner in which contravariant and covariant tensors combine so that all instances of the transformation matrix and its inverse cancel, so that expressions like {v}^i ,mathbf{{e}}_i can immediately be seen to be geometrically identical in all coordinate systems.Similarly, a linear operator, viewed as a geometric object, does not actually depend on a basis: it is just a linear map that accepts a vector as an argument and produces another vector. The transformation law for how the matrix of components of a linear operator changes with the basis is consistent with the transformation law for a contravariant vector, so that the action of a linear operator on a contravariant vector is represented in coordinates as the matrix product of their respective coordinate representations. That is, the components (Tv)^i are given by (Tv)^i = T^i_j v^j. These components transform contravariantly, since
left(widehat{Tv}right)^{i'} = hat{T}^{i'}_{j'} hat{v}^{j'} = left[ left(R^{-1}right)^{i'}_i T^i_j R^j_{j'} right] left[ left(R^{-1}right)^{j'}_j v^j right] = left(R^{-1}right)^{i'}_i (Tv)^i .
The transformation law for an order {{math|p + q}} tensor with p contravariant indices and q covariant indices is thus given as,
hat{T}^{i'_1, ldots, i'_p}_{j'_1, ldots, j'_q} = left(R^{-1}right)^{i'_1}_{i_1} cdots left(R^{-1}right)^{i'_p}_{i_p}

T^{i_1, ldots, i_p}_{j_1, ldots, j_q}

R^{j_1}_{j'_1}cdots R^{j_q}_{j'_q}.
Here the primed indices denote components in the new coordinates, and the unprimed indices denote the components in the old coordinates. Such a tensor is said to be of order or type {{math|(p, q)}}. The terms "order", "type", "rank", "valence", and "degree" are all sometimes used for the same concept. Here, the term "order" or "total order" will be used for the total dimension of the array (or its generalisation in other definitions), {{math|p + q}} in the preceding example, and the term "type" for the pair giving the number of contravariant and covariant indices. A tensor of type {{math|(p, q)}} is also called a {{math|(p, q)}}-tensor for short.This discussion motivates the following formal definition:BOOK, R.W., Sharpe, Differential Geometry: Cartan's Generalization of Klein's Erlangen Program, {{google books, y, Ytqs4xU5QKAC, 194, |date=21 November 2000|publisher=Springer Science & Business Media|isbn=978-0-387-94732-7| page=194}}{{citation|chapter-url={{google books |plainurl=y |id=WROiC9st58gC}}|first=Jan Arnoldus|last=Schouten|authorlink=Jan Arnoldus Schouten|title=Tensor analysis for physicists|year=1954|publisher=Courier Corporation|isbn=978-0-486-65582-6|chapter=Chapter II|url=https://archive.org/details/isbn_9780486655826}}{{quotation|Definition. A tensor of type (p, q) is an assignment of a multidimensional array
T^{i_1dots i_p}_{j_{1}dots j_{q}}[mathbf{f}]
to each basis {{math|f {{=}} (e1, ..., en)}} of an n-dimensional vector space such that, if we apply the change of basis
mathbf{f}mapsto mathbf{f}cdot R = left( mathbf{e}_i R^i_1, dots, mathbf{e}_i R^i_n right)
then the multidimensional array obeys the transformation law
T^{i'_1dots i'_p}_{j'_1dots j'_q}[mathbf{f} cdot R] = left(R^{-1}right)^{i'_1}_{i_1} cdots left(R^{-1}right)^{i'_p}_{i_p}

T^{i_1, ldots, i_p}_{j_1, ldots, j_q}[mathbf{f}]

R^{j_1}_{j'_1}cdots R^{j_q}_{j'_q} .
}}The definition of a tensor as a multidimensional array satisfying a transformation law traces back to the work of Ricci.An equivalent definition of a tensor uses the representations of the general linear group. There is an action of the general linear group on the set of all ordered bases of an n-dimensional vector space. If mathbf f=(mathbf f_1,dots,mathbf f_n) is an ordered basis, and R=(R^i_j) is an invertible ntimes n matrix, then the action is given by
mathbf fR = (mathbf f_iR^i_1,dots,mathbf f_iR^i_n).
Let F be the set of all ordered bases. Then F is a principal homogeneous space for GL(n). Let W be a vector space and let rho be a representation of GL(n) on W (that is, a group homomorphism rho:text{GL}(n)to text{GL}(W)). Then a tensor of type rho is an equivariant map T:Fto W. Equivariance here means that
T(FR) = rho(R^{-1})T(F).
When rho is a tensor representation of the general linear group, this gives the usual definition of tensors as multidimensional arrays. This definition is often used to describe tensors on manifolds,{{citation | last1=Kobayashi|first1=Shoshichi|last2=Nomizu|first2=Katsumi | title = Foundations of Differential Geometry|volume=Vol. 1| publisher=Wiley Interscience | year=1996|edition=New|isbn=978-0-471-15733-5|title-link=Foundations of Differential Geometry}} and readily generalizes to other groups.

As multilinear maps

A downside to the definition of a tensor using the multidimensional array approach is that it is not apparent from the definition that the defined object is indeed basis independent, as is expected from an intrinsically geometric object. Although it is possible to show that transformation laws indeed ensure independence from the basis, sometimes a more intrinsic definition is preferred. One approach that is common in differential geometry is to define tensors relative to a fixed (finite-dimensional) vector space V, which is usually taken to be a particular vector space of some geometrical significance like the tangent space to a manifold.{{citation|last=Lee|first=John|title=Introduction to smooth manifolds|url={{google books |plainurl=y |id=4sGuQgAACAAJ|page=173}}|volume=|page=173|year=2000|publisher=Springer|isbn=978-0-387-95495-0}} In this approach, a type {{nowrap|(p, q)}} tensor T is defined as a multilinear map,
T: underbrace{ V^* timesdotstimes V^*}_{p text{ copies}} times underbrace{ V timesdotstimes V}_{q text{ copies}} rightarrow mathbf{R},
where Vâˆ— is the corresponding dual space of covectors, which is linear in each of its arguments. The above assumes V is a vector space over the real numbers, â„. More generally, V can be taken over an arbitrary field of numbers, F (e.g. the complex numbers) with a one-dimensional vector space over F replacing â„ as the codomain of the multilinear maps.By applying a multilinear map T of type {{nowrap|(p, q)}} to a basis {ej} for V and a canonical cobasis {Îµi} for Vâˆ—,
T^{i_1dots i_p}_{j_1dots j_q} equiv Tleft(boldsymbol{varepsilon}^{i_1}, ldots,boldsymbol{varepsilon}^{i_p}, mathbf{e}_{j_1}, ldots, mathbf{e}_{j_q}right),
a {{nowrap|(p + q)}}-dimensional array of components can be obtained. A different choice of basis will yield different components. But, because T is linear in all of its arguments, the components satisfy the tensor transformation law used in the multilinear array definition. The multidimensional array of components of T thus form a tensor according to that definition. Moreover, such an array can be realized as the components of some multilinear map T. This motivates viewing multilinear maps as the intrinsic objects underlying tensors.In viewing a tensor as a multilinear map, it is conventional to identify the double dual Vâˆ—âˆ— of the vector space V, i.e., the space of linear functionals on the dual vector space Vâˆ—, with the vector space V. There is always a natural linear map from V to its double dual, given by evaluating a linear form in Vâˆ— against a vector in V. This linear mapping is an isomorphism in finite dimensions, and it is often then expedient to identify V with its double dual.

Using tensor products

For some mathematical applications, a more abstract approach is sometimes useful. This can be achieved by defining tensors in terms of elements of tensor products of vector spaces, which in turn are defined through a universal property. A type {{math|(p, q)}} tensor is defined in this context as an element of the tensor product of vector spaces,{{citation|last=Dodson|first=CTJ|title=Tensor geometry|url=https://www.scribd.com/document/344640179/Dodson-Poston-Tensor-Geometry-pdf|volume=130|year=1991|series=GTM|publisher=Springer|last2=Poston |first2=T |p= 105}}{{Springer|id=a/a011120|title=Affine tensor}}
Tin underbrace{V otimesdotsotimes V}_{ptext{ copies}} otimes underbrace{V^* otimesdotsotimes V^*}_{q text{ copies}}.
A basis {{math|v'i}} of {{math|V}} and basis {{math|w'j}} of {{math|W}} naturally induce a basis {{math|v'i âŠ— w'j}} of the tensor product {{math|V âŠ— W}}. The components of a tensor {{math|T}} are the coefficients of the tensor with respect to the basis obtained from a basis {{math|{ei}}} for {{math|V}} and its dual basis {{math|{Îµj}}}, i.e.
T = T^{i_1dots i_p}_{j_1dots j_q}; mathbf{e}_{i_1}otimescdotsotimes mathbf{e}_{i_p}otimes boldsymbol{varepsilon}^{j_1}otimescdotsotimes boldsymbol{varepsilon}^{j_q}.
Using the properties of the tensor product, it can be shown that these components satisfy the transformation law for a type {{math|(p, q)}} tensor. Moreover, the universal property of the tensor product gives a {{math|1}}-to-{{math|1}} correspondence between tensors defined in this way and tensors defined as multilinear maps.Tensor products can be defined in great generality â€“ for example, involving arbitrary modules over a ring. In principle, one could define a "tensor" simply to be an element of any tensor product. However, the mathematics literature usually reserves the term tensor for an element of a tensor product of any number of copies of a single vector space {{math|V}} and its dual, as above.

Tensors in infinite dimensions

This discussion of tensors so far assumes finite dimensionality of the spaces involved, where the spaces of tensors obtained by each of these constructions are naturally isomorphic.The double duality isomorphism, for instance, is used to identify V with the double dual space Vâˆ—âˆ—, which consists of multilinear forms of degree one on Vâˆ—. It is typical in linear algebra to identify spaces that are naturally isomorphic, treating them as the same space. Constructions of spaces of tensors based on the tensor product and multilinear mappings can be generalized, essentially without modification, to vector bundles or coherent sheaves.BOOK, N., Bourbaki, Algebra I: Chapters 1-3, 3, {{google books, y, STS9aZ6F204C, |date=3 August 1998|publisher=Springer Science & Business Media|isbn=978-3-540-64243-5}} where the case of finitely generated projective modules is treated. The global sections of sections of a vector bundle over a compact space form a projective module over the ring of smooth functions. All statements for coherent sheaves are true locally. For infinite-dimensional vector spaces, inequivalent topologies lead to inequivalent notions of tensor, and these various isomorphisms may or may not hold depending on what exactly is meant by a tensor (see topological tensor product). In some applications, it is the tensor product of Hilbert spaces that is intended, whose properties are the most similar to the finite-dimensional case. A more modern view is that it is the tensors' structure as a symmetric monoidal category that encodes their most important properties, rather than the specific models of those categories.{{citation|title= Braided tensor categories |first1= A |last1=Joyal |first2= Ross |last2=Street |journal= Advances in Mathematics |year=1993 |volume=102 |pages= 20â€“78|doi= 10.1006/aima.1993.1055 }}

Tensor fields

In many applications, especially in differential geometry and physics, it is natural to consider a tensor with components that are functions of the point in a space. This was the setting of Ricci's original work. In modern mathematical terminology such an object is called a tensor field, often referred to simply as a tensor.In this context, a coordinate basis is often chosen for the tangent vector space. The transformation law may then be expressed in terms of partial derivatives of the coordinate functions,
bar{x}^ileft(x^1, ldots, x^nright),
defining a coordinate transformation,
hat{T}^{i'_1dots i'_p}_{j'_1dots j'_q}left(bar{x}^1, ldots, bar{x}^nright) =
frac{partial bar{x}^{i'_1}}{partial x^{i_1}}
cdots
frac{partial bar{x}^{i'_p}}{partial x^{i_p}}
frac{partial x^{j_1}}{partial bar{x}^{j'_1}}
cdots
frac{partial x^{j_q}}{partial bar{x}^{j'_q}}
T^{i_1dots i_p}_{j_1dots j_q}left(x^1, ldots, x^nright).

Examples

{{See also|Dyadic tensor}}This table shows important examples of tensors on vector spaces and tensor fields on manifolds. The tensors are classified according to their type {{math|(n, m)}}, where n is the number of contravariant indices, m is the number of covariant indices, and {{math|n + m}} gives the total order of the tensor. For example, a bilinear form is the same thing as a {{math|(0, 2)}}-tensor; an inner product is an example of a {{math|(0, 2)}}-tensor, but not all {{math|(0, 2)}}-tensors are inner products. In the {{math|(0, M)}}-entry of the table, M denotes the dimensionality of the underlying vector space or manifold because for each dimension of the space, a separate index is needed to select that dimension to get a maximally covariant antisymmetric tensor.
{| class="wikitable"! colspan=2 rowspan=2 width="75px" |! colspan=7 | m
! scope="col" width="175px" | 0! scope="col" width="175px" | 1! scope="col" width="175px" | 2! scope="col" width="175px" | 3! scope="col" width="75px" | â‹¯! scope="col" width="175px" | M! scope="col" width="75px" | â‹¯
! rowspan=6 | n! scope="row" | 0
Scalar (mathematics)>Scalar, e.g. scalar curvatureCovector, linear functional, 1-form, e.g. multipole expansion>dipole moment, gradient of a scalar field| Bilinear form, e.g. inner product, quadrupole moment, metric tensor, Ricci curvature, 2-form, symplectic formmultipole moment>octupole moment|| E.g. M-form i.e. volume form|
! scope="row" | 1| Euclidean vector
Linear transformation,PAUL FIRST2=SHLOMO TITLE=A COURSE IN MATHEMATICS FOR STUDENTS OF PHYSICS: VOLUME 2PUBLISHER=CAMBRIDGE UNIVERSITY PRESSPAGE=669PLAINURL=Y, WgZ3Ia0SPE8CA, }} Kronecker delta| E.g. cross product in three dimensions| E.g. Riemann curvature tensor|||
! scope="row" | 2| Inverse metric tensor, bivector, e.g., Poisson structure|| E.g. elasticity tensor||||
! scope="row" | â‹®|||||||
! scope="row" | N|Multivector||||||
! scope="row" | â‹®|||||||
There is also something called the Gyration tensor.Raising an index on an {{math|(n, m)}}-tensor produces an {{math|(n + 1, m âˆ’ 1)}}-tensor; this corresponds to moving diagonally down and to the left on the table. Symmetrically, lowering an index corresponds to moving diagonally up and to the right on the table. Contraction of an upper with a lower index of an {{math|(n, m)}}-tensor produces an {{math|(n âˆ’ 1, m âˆ’ 1)}}-tensor; this corresponds to moving diagonally up and to the left on the table.{{Clear}}
{{multiple image
| align = right
| footer = Geometric interpretation of grade n elements in a real exterior algebra for {{math|1=n = 0}} (signed point), 1 (directed line segment, or vector), 2 (oriented plane element), 3 (oriented volume). The exterior product of n vectors can be visualized as any n-dimensional shape (e.g. n-parallelotope, n-ellipsoid); with magnitude (hypervolume), and orientation defined by that on its {{math|n âˆ’ 1}}-dimensional boundary and on which side the interior is.BOOK, R., Penrose, The Road to Reality, Vintage books, 2007, 978-0-679-77631-4, The Road to Reality, BOOK, Gravitation, J.A., Wheeler, C., Misner, K.S., Thorne, W.H. Freeman & Co, 1973, 83, 978-0-7167-0344-0, {{google books, y, w4Gigq3tY1kC, }}
| width1 = 220
| image1 = N vector positive.svg
| caption1 = Orientation defined by an ordered set of vectors.
| width2 = 220
| image2 = N vector negative.svg
| caption2 = Reversed orientation corresponds to negating the exterior product.
}}

Notation

There are several notational systems that are used to describe tensors and perform calculations involving them.

Ricci calculus

Ricci calculus is the modern formalism and notation for tensor indices: indicating inner and outer products, covariance and contravariance, summations of tensor components, symmetry and antisymmetry, and partial and covariant derivatives.

Einstein summation convention

The Einstein summation convention dispenses with writing summation signs, leaving the summation implicit. Any repeated index symbol is summed over: if the index {{mvar|i}} is used twice in a given term of a tensor expression, it means that the term is to be summed for all {{mvar|i}}. Several distinct pairs of indices may be summed this way.

Penrose graphical notation

Penrose graphical notation is a diagrammatic notation which replaces the symbols for tensors with shapes, and their indices by lines and curves. It is independent of basis elements, and requires no symbols for the indices.

Abstract index notation

The abstract index notation is a way to write tensors such that the indices are no longer thought of as numerical, but rather are indeterminates. This notation captures the expressiveness of indices and the basis-independence of index-free notation.

Component-free notation

A component-free treatment of tensors uses notation that emphasises that tensors do not rely on any basis, and is defined in terms of the tensor product of vector spaces.

Operations

There are several operations on tensors that again produce a tensor. The linear nature of tensor implies that two tensors of the same type may be added together, and that tensors may be multiplied by a scalar with results analogous to the scaling of a vector. On components, these operations are simply performed component-wise. These operations do not change the type of the tensor; but there are also operations that produce a tensor of different type.

Tensor product

The tensor product takes two tensors, S and T, and produces a new tensor, {{nowrap|{{math|S âŠ— T}}}}, whose order is the sum of the orders of the original tensors. When described as multilinear maps, the tensor product simply multiplies the two tensors, i.e.
(Sotimes T)(v_1,ldots, v_n, v_{n+1},ldots, v_{n+m}) = S(v_1,ldots, v_n)T( v_{n+1},ldots, v_{n+m}),
which again produces a map that is linear in all its arguments. On components, the effect is to multiply the components of the two input tensors pairwise, i.e.
(Sotimes T)^{i_1ldots i_l i_{l+1}ldots i_{l+n}}_{j_1ldots j_k j_{k+1}ldots j_{k+m}} =
S^{i_1ldots i_l}_{j_1ldots j_k} T^{i_{l+1}ldots i_{l+n}}_{j_{k+1}ldots j_{k+m}},If {{mvar|S}} is of type {{math|(l, k)}} and {{mvar|T}} is of type {{math|(n, m)}}, then the tensor product {{nowrap|{{math|S âŠ— T}}}} has type {{nowrap|{{math|(l + n, k + m)}}}}.

Contraction

Tensor contraction is an operation that reduces a type {{nowrap|(n, m)}} tensor to a type {{nowrap|(n âˆ’ 1, m âˆ’ 1)}} tensor, of which the trace is a special case. It thereby reduces the total order of a tensor by two. The operation is achieved by summing components for which one specified contravariant index is the same as one specified covariant index to produce a new component. Components for which those two indices are different are discarded. For example, a {{nowrap|(1, 1)}}-tensor T_i^j can be contracted to a scalar through
T_i^i.
Where the summation is again implied. When the {{nowrap|(1, 1)}}-tensor is interpreted as a linear map, this operation is known as the trace.The contraction is often used in conjunction with the tensor product to contract an index from each tensor.The contraction can also be understood using the definition of a tensor as an element of a tensor product of copies of the space V with the space Vâˆ— by first decomposing the tensor into a linear combination of simple tensors, and then applying a factor from Vâˆ— to a factor from V. For example, a tensor
T in Votimes Votimes V^*
can be written as a linear combination
T=v_1otimes w_1otimes alpha_1 + v_2otimes w_2otimes alpha_2 +cdots + v_Notimes w_Notimes alpha_N.
The contraction of T on the first and last slots is then the vector
alpha_1(v_1)w_1 + alpha_2(v_2)w_2+cdots+alpha_N(v_N)w_N.
In a vector space with an inner product (also known as a metric) g, the term contraction is used for removing two contravariant or two covariant indices by forming a trace with the metric tensor or its inverse. For example, a {{nowrap|(2, 0)}}-tensor T^{ij} can be contracted to a scalar through
T^{ij} g_{ij}
(yet again assuming the summation convention).

Raising or lowering an index

When a vector space is equipped with a nondegenerate bilinear form (or metric tensor as it is often called in this context), operations can be defined that convert a contravariant (upper) index into a covariant (lower) index and vice versa. A metric tensor is a (symmetric) ({{nowrap|0, 2)}}-tensor; it is thus possible to contract an upper index of a tensor with one of the lower indices of the metric tensor in the product. This produces a new tensor with the same index structure as the previous tensor, but with lower index generally shown in the same position of the contracted upper index. This operation is quite graphically known as lowering an index.Conversely, the inverse operation can be defined, and is called raising an index. This is equivalent to a similar contraction on the product with a {{nowrap|(2, 0)}}-tensor. This inverse metric tensor has components that are the matrix inverse of those of the metric tensor.

Applications

Continuum mechanics

Important examples are provided by continuum mechanics. The stresses inside a solid body or fluid are described by a tensor field. The stress tensor and strain tensor are both second-order tensor fields, and are related in a general linear elastic material by a fourth-order elasticity tensor field. In detail, the tensor quantifying stress in a 3-dimensional solid object has components that can be conveniently represented as a 3â€‰Ã—â€‰3 array. The three faces of a cube-shaped infinitesimal volume segment of the solid are each subject to some given force. The force's vector components are also three in number. Thus, 3â€‰Ã—â€‰3, or 9 components are required to describe the stress at this cube-shaped infinitesimal segment. Within the bounds of this solid is a whole mass of varying stress quantities, each requiring 9 quantities to describe. Thus, a second-order tensor is needed.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 {{nowrap|(2, 0)}}, in linear elasticity, or more precisely by a tensor field of type {{nowrap|(2, 0)}}, since the stresses may vary from point to point.

Other examples from physics

Common applications include:

Applications of tensors of order > 2

The concept of a tensor of order two is often conflated with that of a matrix. Tensors of higher order do however capture ideas important in science and engineering, as has been shown successively in numerous areas as they develop. This happens, for instance, in the field of computer vision, with the trifocal tensor generalizing the fundamental matrix.The field of nonlinear optics studies the changes to material polarization density under extreme electric fields. The polarization waves generated are related to the generating electric fields through the nonlinear susceptibility tensor. If the polarization P is not linearly proportional to the electric field E, the medium is termed nonlinear. To a good approximation (for sufficiently weak fields, assuming no permanent dipole moments are present), P is given by a Taylor series in E whose coefficients are the nonlinear susceptibilities:
frac{P_i}{varepsilon_0} = sum_j chi^{(1)}_{ij} E_j + sum_{jk} chi_{ijk}^{(2)} E_j E_k + sum_{jkell} chi_{ijkell}^{(3)} E_j E_k E_ell + cdots. !
Here chi^{(1)} is the linear susceptibility, chi^{(2)} gives the Pockels effect and second harmonic generation, and chi^{(3)} gives the Kerr effect. This expansion shows the way higher-order tensors arise naturally in the subject matter.

Generalizations

Holors

As discussed above, a tensor can be represented as a (potentially multidimensional, multi-indexed) array of quantities. To distinguish tensors (when denoted as tensorial arrays of quantities with respect to a fixed basis) from arbitrary arrays of quantities the term holor was coined for the latter.BOOK
, Moon
, Parry Hiram
, Parry Moon
, Spencer
, Domina Eberle
, Domina Eberle Spencer
, Theory of Holors: A Generalization of Tensors
, Cambridge University Press
, 1986
, 978-0-521-01900-2
,
So tensors can be analyzed as a particular type of holor, alongside other not strictly tensorial holors, such as neural network (node and/or link) values, indexed inventory tables, and so on. Another group of holors that transform like tensors up to a so-called weight, derived from the transformation equations, are the tensor densities, e.g. the Levi-Civita Symbol. The Christoffel symbols also belong to the holors.The term holor is not in widespread use, and unfortunately the word "tensor" is often misused when referring to the multidimensional array representation of a holor, causing confusion regarding the strict meaning of tensor.The concept of holors and the associated terminology provide an algebra and calculus for holors in a more general setting than what is seen for tensorial arrays.

Tensor products of vector spaces

The vector spaces of a tensor product need not be the same, and sometimes the elements of such a more general tensor product are called "tensors". For example, an element of the tensor product space {{math|V âŠ— W}} is a second-order "tensor" in this more general sense,BOOK, M. D., Maia, Geometry of the Fundamental Interactions: On Riemann's Legacy to High Energy Physics and Cosmology, {{google books, y, wEWw_vGBDW8C, 48, publisher=Springer Science & Business Mediapage=48}} and an order-{{mathd}} tensor may likewise be defined as an element of a tensor product of {{math>d}} different vector spaces.{{GOOGLE BOOKS >PLAINURL=Y PAGE=7, last=publisher=CRC Pressisbn=978-1-4665-0729-6editor-first=Leslieedition=2ndpages=15â€“7}} A type {{mathn, m)}} tensor, in the sense defined previously, is also a tensor of order {{math>n + m}} in this more general sense. The concept of tensor product tensor product of modules to arbitrary module over a ring>modules over a ring.

Tensors in infinite dimensions

The notion of a tensor can be generalized in a variety of ways to infinite dimensions. One, for instance, is via the tensor product of Hilbert spaces.JOURNAL, Segal, I. E., January 1956, Tensor Algebras Over Hilbert Spaces. I, Transactions of the American Mathematical Society, 81, 1, 106â€“134, 1992855, 10.2307/1992855, Another way of generalizing the idea of tensor, common in nonlinear analysis, is via the multilinear maps definition where instead of using finite-dimensional vector spaces and their algebraic duals, one uses infinite-dimensional Banach spaces and their continuous dual.BOOK, Abraham, Ralph, Marsden, Jerrold E., Ratiu, Tudor S.plainurl=y, dWHet_zgyCAC, |title=Manifolds, Tensor Analysis and Applications|edition=2nd|series=Applied Mathematical Sciences, v. 75|volume=75|date= February 1988|origyear=First Edition 1983|publisher=Springer-Verlag|location=New York|isbn=978-0-387-96790-5|oclc= 18562688|pages=338â€“339|chapter=Chapter 5 Tensors|quote=Elements of Trs are called tensors on E, [...].}} Tensors thus live naturally on Banach manifoldsBOOK, Lang, Serge, Serge Lang, Differential manifolds, {{google books, y, dn7rBwAAQBAJ, | publisher=Addison-Wesley Pub. Co. | year=1972 |isbn= 978-0-201-04166-8 |location=Reading, Massachusetts}} and FrÃ©chet manifolds.

Tensor densities

Suppose that a homogeneous medium fills {{math|R3}}, so that the density of the medium is described by a single scalar value {{math|Ï}} in {{math|kg mâˆ’3}}. The mass, in kg, of a region {{math|Î©}} is obtained by multiplying {{math|Ï}} by the volume of the region {{math|Î©}}, or equivalently integrating the constant {{math|Ï}} over the region:
m=int_Omega rho, dx,dy,dz
where the Cartesian coordinates {{math|xyz}} are measured in m. If the units of length are changed into cm, then the numerical values of the coordinate functions must be rescaled by a factor of 100:
The numerical value of the density {{math|Ï}} must then also transform by 100^{-3}m^3/cm^3 to compensate, so that the numerical value of the mass in kg is still given by integral of rho, dx,dy,dz. Thus rho'=100^{-3}rho (in units of {{math|kg cmâˆ’3}}).More generally, if the Cartesian coordinates {{math|xyz}} undergo a linear transformation, then the numerical value of the density {{math|Ï}} must change by a factor of the reciprocal of the absolute value of the determinant of the coordinate transformation, so that the integral remains invariant, by the change of variables formula for integration. Such a quantity that scales by the reciprocal of the absolute value of the determinant of the coordinate transition map is called a scalar density. To model a non-constant density, {{math|Ï}} is a function of the variables {{math|xyz}} (a scalar field), and under a curvilinear change of coordinates, it transforms by the reciprocal of the Jacobian of the coordinate change. For more on the intrinsic meaning, see Density on a manifold.A tensor density transforms like a tensor under a coordinate change, except that it in addition picks up a factor of the absolute value of the determinant of the coordinate transition:{{citation|first=Jan Arnoldus|last=Schouten|authorlink=Jan Arnoldus Schouten|url={{google books |plainurl=y |id=WROiC9st58gC}}|title=Tensor analysis for physicists}}, Â§II.8: Densities.
T^{i'_1dots i'_p}_{j'_1dots j'_q}[mathbf{f} cdot R] = |det R|^{-w}left(R^{-1}right)^{i'_1}_{i_1} cdots left(R^{-1}right)^{i'_p}_{i_p}

T^{i_1, ldots, i_p}_{j_1, ldots, j_q}[mathbf{f}]

R^{j_1}_{j'_1}cdots R^{j_q}_{j'_q} .
Here w is called the weight. In general, any tensor multiplied by a power of this function or its absolute value is called a tensor density, or a weighted tensor.BOOK, Applications of tensor analysis, AJ, McConnell, {{google books, y, ZCP0AwAAQBAJ, |publisher=Dover|year=1957|page=28}}{{sfn|Kay|1988|p=27}} An example of a tensor density is the current density of electromagnetism.Under an affine transformation of the coordinates, a tensor transforms by the linear part of the transformation itself (or its inverse) on each index. These come from the rational representations of the general linear group. But this is not quite the most general linear transformation law that such an object may have: tensor densities are non-rational, but are still semisimple representations. A further class of transformations come from the logarithmic representation of the general linear group, a reducible but not semisimple representation,{{citation|first=Peter |last=Olver|title=Equivalence, invariants, and symmetry|url={{google books |plainurl=y |id=YuTzf61HILAC|page=77}}|page=77|publisher=Cambridge University Press|year=1995}} consisting of an {{math|(x,y) âˆˆ R2}} with the transformation law
(x,y)mapsto (x+ylog|det R|,y).

Geometric objects

The transformation law for a tensor behaves as a functor on the category of admissible coordinate systems, under general linear transformations (or, other transformations within some class, such as local diffeomorphisms.) This makes a tensor a special case of a geometrical object, in the technical sense that it is a function of the coordinate system transforming functorially under coordinate changes.Haantjes, J., & Laman, G. (1953). On the definition of geometric objects. I. Examples of objects obeying more general kinds of transformation laws are jets and, more generally still, natural bundles.{{citation|first=Albert|last=Nijenhuis|authorlink=Albert Nijenhuis|chapter-url=http://www.mathunion.org/ICM/ICM1958/Main/icm1958.0463.0469.ocr.pdf|chapter=Geometric aspects of formal differential operations on tensor fields|title= Proc. Internat. Congress Math.(Edinburgh, 1958)|year=1960|publisher=Cambridge University Press|pages=463â€“469}}.{{citation|url=https://projecteuclid.org/download/pdf_1/euclid.jdg/1214430830|title=On the theory of geometric objects|first=Sarah |last=Salviori|journal=Journal of Differential Geometry|year=1972|volume=7|issue=1â€“2|pages=257â€“278|doi=10.4310/jdg/1214430830}}.

Spinors

When changing from one orthonormal basis (called a frame) to another by a rotation, the components of a tensor transform by that same rotation. This transformation does not depend on the path taken through the space of frames. However, the space of frames is not simply connected (see orientation entanglement and plate trick): there are continuous paths in the space of frames with the same beginning and ending configurations that are not deformable one into the other. It is possible to attach an additional discrete invariant to each frame that incorporates this path dependence, and which turns out (locally) to have values of Â±1.BOOK, The road to reality: a complete guide to the laws of our universe, {{google books, y, VWTNCwAAQBAJ, 203, |first=Roger|last=Penrose|authorlink=Roger Penrose|publisher=Knopf|year=2005|pages=203â€“206}} A spinor is an object that transforms like a tensor under rotations in the frame, apart from a possible sign that is determined by the value of this discrete invariant.{{citation|first=E. |last=Meinrenken|title=Clifford Algebras and Lie Theory|chapter=The spin representation|series=Ergebnisse der Mathematik undihrer Grenzgebiete. 3. Folge / A Series of Modern Surveys in Mathematics|volume=58|pages=49â€“85|doi=10.1007/978-3-642-36216-3_3|publisher=Springer-Verlag|year=2013|isbn=978-3-642-36215-6}}{{citation|first=S. H. |last=Dong|chapter-url=https://www.springer.com/us/book/9789400719163Fricci|title=Wave Equations in Higher Dimensions|chapter=Chapter 2, Special Orthogonal Group SO(N)|publisher=Springer|year=2011|pages=13â€“38}}Succinctly, spinors are elements of the spin representation of the rotation group, while tensors are elements of its tensor representations. Other classical groups have tensor representations, and so also tensors that are compatible with the group, but all non-compact classical groups have infinite-dimensional unitary representations as well.

History

The concepts of later tensor analysis arose from the work of Carl Friedrich Gauss in differential geometry, and the formulation was much influenced by the theory of algebraic forms and invariants developed during the middle of the nineteenth century.BOOK
, Karin, Reich
, Die Entwicklung des TensorkalkÃ¼ls
, 1994
, BirkhÃ¤user
, 978-3-7643-2814-6
, Science networks historical studies, v. 11

, 31468174
, {hide}google books, y, O6lixBzbc0gC,
{edih} The word "tensor" itself was introduced in 1846 by William Rowan HamiltonJOURNAL
, William Rowan, Hamilton
, On some Extensions of Quaternions
, Philosophical Magazine
, 1854â€“1855
, 492â€“499, 125â€“137, 261â€“269, 46â€“51, 280â€“290
, David R., Wilkins
, 7â€“9
, 0302-7597
, From p. 498: "And if we agree to call the square root (taken with a suitable sign) of this scalar product of two conjugate polynomes, P and KP, the common TENSOR of each, â€¦ " to describe something different from what is now meant by a tensor.Namely, the norm operation in a certain type of algebraic system (now known as a Clifford algebra). The contemporary usage was introduced by Woldemar Voigt in 1898.BOOK, Woldemar, Voigt, Die fundamentalen physikalischen Eigenschaften der Krystalle in elementarer Darstellung [The fundamental physical properties of crystals in an elementary presentation], {{google books, y, QhBDAAAAIAAJ, 20, |year=1898|publisher=Von Veit|pages=20â€“|quote= Wir wollen uns deshalb nur darauf stÃ¼tzen, dass ZustÃ¤nde der geschilderten Art bei Spannungen und Dehnungen nicht starrer KÃ¶rper auftreten, und sie deshalb tensorielle, die fÃ¼r sie charakteristischen physikalischen GrÃ¶ssen aber Tensoren nennen. [We therefore want [our presentation] to be based only on [the assumption that] conditions of the type described occur during stresses and strains of non-rigid bodies, and therefore call them "tensorial" but call the characteristic physical quantities for them "tensors".]}}Tensor calculus was developed around 1890 by Gregorio Ricci-Curbastro under the title absolute differential calculus, and originally presented by Ricci in 1892.JOURNAL
, G., Ricci Curbastro
, RÃ©sumÃ© de quelques travaux sur les systÃ¨mes variables de fonctions associÃ©s Ã  une forme diffÃ©rentielle quadratique
, {hide}google books, y, 1bGdAQAACAAJ,
|journal=Bulletin des Sciences MathÃ©matiques
|volume=2
|pages=167â€“189
|year=1892
|issue=16
{edih} It was made accessible to many mathematicians by the publication of Ricci and Tullio Levi-Civita's 1900 classic text MÃ©thodes de calcul diffÃ©rentiel absolu et leurs applications (Methods of absolute differential calculus and their applications).{{sfn|Ricci|Levi-Civita|1900}}In the 20th century, the subject came to be known as tensor analysis, and 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. Einstein had learned about them, with great difficulty, from the geometer Marcel Grossmann.BOOK
, Abraham, Pais
, Subtle Is the Lord: The Science and the Life of Albert Einstein
, Oxford University Press
, 2005
, 978-0-19-280672-7
, {hide}google books, y, U2mO4nUunuwC,

Notes

{{Reflist|group="Note"}}

References

{{Reflist|30em}}

General

• BOOK

, Bishop
, Richard L., Richard L. Bishop
, Samuel I. Goldberg
, Tensor Analysis on Manifolds
, 1980
, Dover
, {{google books, y, ePFIAwAAQBAJ,
| isbn = 978-0-486-64039-6
| origyear = 1968
}}
• BOOK

, Danielson
, Donald A.
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, 2/e
, 2003
, {{google books, y, A9fiXTC3cxsC,
| publisher = Westview (Perseus)
| isbn = 978-0-8133-4080-7
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}}
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,
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, D. F.
, Introduction to Tensor Calculus, Relativity and Cosmology
, 3/e
, {{google books, y, rJYoAwAAQBAJ,
| year= 2003
| publisher = Dover
| isbn = 978-0-486-42540-5
}}
• BOOK

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| publisher = Dover
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| origyear = 1975
}}
• BOOK, James R., Munkres, Analysis On Manifolds, {{google books, y, tGT6K6HdFfwC, |date=7 July 1997|publisher=Avalon Publishing|isbn=978-0-8133-4548-2}} Chapter six gives a "from scratch" introduction to covariant tensors.
• JOURNAL, MÃ©thodes de calcul diffÃ©rentiel absolu et leurs applications, Ricci, Gregorio, Gregorio Ricci-Curbastro, Levi-Civita, Tullio, Mathematische Annalen, 54, 1â€“2, March 1900, 125â€“201, 10.1007/BF01454201, harv,weblink
,
• BOOK, Kay, David C, Schaum's Outline of Tensor Calculus, McGraw-Hill, 1988-04-01, 978-0-07-033484-7, {{google books, y, 6tUU3KruG14C, |ref=harv}}
• BOOK, Bernard F., Schutz, Geometrical Methods of Mathematical Physics, {{google books, y, HAPMB2e643kC, |date=28 January 1980|publisher=Cambridge University Press|isbn=978-0-521-29887-2}}
• BOOK, John Lighton, Synge, Alfred, Schild, Tensor Calculus, {{google books, y, 8vlGhlxqZjsC, |year=1969|publisher=Courier Corporation|isbn=978-0-486-63612-2}}

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