Let be a vectorial space on a commutative field . The vectorial
space of
the linear forms on is called the dual of and is noted .
When has a finite dimension, then has also a finite
dimension and its dimension is equal to the dimension of .
If has an infinite dimension, has also an infinite
dimension but the two spaces are not isomorphic.
In this appendix, we introduce the fundamental notion of
tensor\index{tensor} in
physics. More information can be found in ([#References|references])
for instance.
Let be a finite dimension vectorial space.
Let be a basis of .
A vector of can be referenced by its components is the basis
:
In this chapter the repeated index convention (or {\bf Einstein summing convention})
will be used. It consists in considering that a product of two quantities with
the same index correspond to a sum over this index. For instance:
or
To the vectorial space corresponds a space called the dual of . A
element of is a linear form on : it is a linear mapping
that maps any vector of to a real. is defined by a set of number
because the most general form of a linear form on is:
A basis of can be defined by the following linear form
where is one if and zero if not.
Thus to each vector of of components can be associated a dual
vector in of components :
The quantity
is an invariant. It is independent on the basis chosen.
On another hand, the expression of the components of vector depend on the
basis chosen. If defines a transformation that maps basis
to another basis
eqcov
we have the following relation between components
of in and of in :
eqcontra
This comes from the identification of
and
Equations eqcov and eqcontra define two types of variables:
covariant variables that are transformed like the vector basis.
are such
variables. Contravariant variables that are transformed like the
components of a vector on this basis.
Using a physicist vocabulary is called a covariant vector and a
contravariant vector.
Let and two vectors of two vectorial spaces and
.
The tensorial product space is the vectorial space
such that there exist a unique isomorphism between the space of the
bilinear forms of and the linear forms of .
A bilinear form of is:
It can be considered as a linear form of using
application from to that is
linear and distributive with respect to . If is a basis of
and a basis of , then
is a basis of .
Thus tensor is an element of .
A second order covariant tensor is thus an element of .
In a change of basis, its components are transformed according the
following relation:
Now we can define a tensor on any rank of any variance. For instance a tensor
of third order two times covariant and one time contravariant is an element
of and noted .
A second order tensor is called symmetric if . It is called
antisymmetric is .
Pseudo tensors are transformed slightly differently from ordinary tensors. For
instance a second order covariant pseudo tensor is transformed according to:
where is the determinant of transformation .
secformultens
Let us introduce two particular tensors.
The Kronecker symbol is defined by:
It is the only second order tensor invariant in by rotations.
The signature of permutations tensor is defined by:
It is the only pseudo tensor of rank 3 invariant by rotations in . It verifies the equality:
Let us introduce two tensor operations: scalar product, vectorial product.
Scalar product is the contraction of vectors and :
vectorial product of two vectors and is:
From those definitions, following formulas can be showed:
Green's theorem allows one to transform a volume calculation integral into a
surface calculation integral.
Theorem:
Let be a bounded domain of with a regular boundary. Let be the unitary vector normal to hypersurface
(oriented towards the exterior of ). Let be a tensor,
continuously derivable in , then:\index{Green's theorem}
Here are some important Green's formulas obtained by applying Green's theorem: