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  2. Divergence - Wikipedia

    en.wikipedia.org/wiki/Divergence

    In vector calculus, divergence is a vector operator that operates on a vector field, producing a scalar field giving the quantity of the vector field's source at each point. More technically, the divergence represents the volume density of the outward flux of a vector field from an infinitesimal volume around a given point.

  3. Vector calculus identities - Wikipedia

    en.wikipedia.org/wiki/Vector_calculus_identities

    Specifically, the divergence of a vector is a scalar. The divergence of a higher-order tensor field may be found by decomposing the tensor field into a sum of outer products and using the identity, = + where is the directional derivative in the direction of multiplied by its magnitude.

  4. Divergence theorem - Wikipedia

    en.wikipedia.org/wiki/Divergence_theorem

    In vector calculus, the divergence theorem, also known as Gauss's theorem or Ostrogradsky's theorem, [1] is a theorem relating the flux of a vector field through a closed surface to the divergence of the field in the volume enclosed.

  5. Solenoidal vector field - Wikipedia

    en.wikipedia.org/wiki/Solenoidal_vector_field

    An example of a solenoidal vector field, (,) = (,) In vector calculus a solenoidal vector field (also known as an incompressible vector field , a divergence-free vector field , or a transverse vector field ) is a vector field v with divergence zero at all points in the field: ∇ ⋅ v = 0. {\displaystyle \nabla \cdot \mathbf {v} =0.}

  6. Vector field - Wikipedia

    en.wikipedia.org/wiki/Vector_field

    A vector field V defined on an open set S is called a gradient field or a conservative field if there exists a real-valued function (a scalar field) f on S such that = = (,,, …,). The associated flow is called the gradient flow , and is used in the method of gradient descent .

  7. Green's identities - Wikipedia

    en.wikipedia.org/wiki/Green's_identities

    This identity is derived from the divergence theorem applied to the vector field F = ψ ∇φ while using an extension of the product rule that ∇ ⋅ (ψ X) = ∇ψ ⋅X + ψ ∇⋅X: Let φ and ψ be scalar functions defined on some region U ⊂ R d, and suppose that φ is twice continuously differentiable, and ψ is once continuously differentiable.

  8. Helmholtz decomposition - Wikipedia

    en.wikipedia.org/wiki/Helmholtz_decomposition

    In other words, a vector field can be constructed with both a specified divergence and a specified curl, and if it also vanishes at infinity, it is uniquely specified by its divergence and curl. This theorem is of great importance in electrostatics , since Maxwell's equations for the electric and magnetic fields in the static case are of ...

  9. Del - Wikipedia

    en.wikipedia.org/wiki/Del

    When applied to a field (a function defined on a multi-dimensional domain), it may denote any one of three operations depending on the way it is applied: the gradient or (locally) steepest slope of a scalar field (or sometimes of a vector field, as in the Navier–Stokes equations); the divergence of a vector field; or the curl (rotation) of a ...