Partial derivative in the context of "Surface area"

In systems theory, a system or a process is in a steady state if the variables (called state variables) which define the behavior of the system or the process are unchanging in time. In continuous time, this means that for those properties p of the system, the partial derivative with respect to time is zero and remains so:

In discrete time, it means that the first difference of each property is zero and remains so:

In mathematics, a partial differential equation (PDE) is an equation which involves a multivariable function and one or more of its partial derivatives.

The function is often thought of as an "unknown" that solves the equation, similar to how x is thought of as an unknown number solving, e.g., an algebraic equation like x − 3x + 2 = 0. However, it is usually impossible to write down explicit formulae for solutions of partial differential equations. There is correspondingly a vast amount of modern mathematical and scientific research on methods to numerically approximate solutions of certain partial differential equations using computers. Partial differential equations also occupy a large sector of pure mathematical research, in which the usual questions are, broadly speaking, on the identification of general qualitative features of solutions of various partial differential equations, such as existence, uniqueness, regularity and stability. Among the many open questions are the existence and smoothness of solutions to the Navier–Stokes equations, named as one of the Millennium Prize Problems in 2000.

Vector calculus or vector analysis is a branch of mathematics concerned with the differentiation and integration of vector fields, primarily in three-dimensional Euclidean space, ${\displaystyle \mathbb {R} ^{3}.}$ The term vector calculus is sometimes used as a synonym for the broader subject of multivariable calculus, which spans vector calculus as well as partial differentiation and multiple integration. Vector calculus plays an important role in differential geometry and in the study of partial differential equations. It is used extensively in physics and engineering, especially in the description of electromagnetic fields, gravitational fields, and fluid flow.

Vector calculus was developed from the theory of quaternions by J. Willard Gibbs and Oliver Heaviside near the end of the 19th century, and most of the notation and terminology was established by Gibbs and Edwin Bidwell Wilson in their 1901 book, Vector Analysis, though earlier mathematicians such as Isaac Newton pioneered the field. In its standard form using the cross product, vector calculus does not generalize to higher dimensions, but the alternative approach of geometric algebra, which uses the exterior product, does (see § Generalizations below for more).

In mathematics, the Hessian matrix, Hessian or (less commonly) Hesse matrix is a square matrix of second-order partial derivatives of a scalar-valued function, or scalar field. It describes the local curvature of a function of many variables. The Hessian matrix was developed in the 19th century by the German mathematician Ludwig Otto Hesse and later named after him. Hesse originally used the term "functional determinants". The Hessian is sometimes denoted by H or ${\displaystyle \nabla \nabla }$ or ${\displaystyle \nabla ^{2}}$ or ${\displaystyle \nabla \otimes \nabla }$ or ${\displaystyle D^{2}}$.

In differential calculus, there is no single standard notation for differentiation. Instead, several notations for the derivative of a function or a dependent variable have been proposed by various mathematicians, including Leibniz, Newton, Lagrange, and Arbogast. The usefulness of each notation depends on the context in which it is used, and it is sometimes advantageous to use more than one notation in a given context. For more specialized settings—such as partial derivatives in multivariable calculus, tensor analysis, or vector calculus—other notations, such as subscript notation or the ∇ operator are common. The most common notations for differentiation (and its opposite operation, antidifferentiation or indefinite integration) are listed below.

In mathematics, the Laplace operator or Laplacian is a differential operator given by the divergence of the gradient of a scalar function on Euclidean space. It is usually denoted by the symbols ⁠${\displaystyle \nabla \cdot \nabla }$⁠, ${\displaystyle \nabla ^{2}}$ (where ${\displaystyle \nabla }$ is the nabla operator), or ⁠${\displaystyle \Delta }$⁠. In a Cartesian coordinate system, the Laplacian is given by the sum of second partial derivatives of the function with respect to each independent variable. In other coordinate systems, such as cylindrical and spherical coordinates, the Laplacian also has a useful form. Informally, the Laplacian Δf (p) of a function f at a point p measures by how much the average value of f over small spheres or balls centered at p deviates from f (p).

The Laplace operator is named after the French mathematician Pierre-Simon de Laplace (1749–1827), who first applied the operator to the study of celestial mechanics: the Laplacian of the gravitational potential due to a given mass density distribution is a constant multiple of that density distribution. Solutions of Laplace's equation Δf = 0 are called harmonic functions and represent the possible gravitational potentials in regions of vacuum.

Del, or nabla, is an operator used in mathematics (particularly in vector calculus) as a vector differential operator, usually represented by ∇ (the nabla symbol). When applied to a function defined on a one-dimensional domain, it denotes the standard derivative of the function as defined in calculus. 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 vector field.

Del is a very convenient mathematical notation for those three operations (gradient, divergence, and curl) that makes many equations easier to write and remember. The del symbol (or nabla) can be formally defined as a vector operator whose components are the corresponding partial derivative operators. As a vector operator, it can act on scalar and vector fields in three different ways, giving rise to three different differential operations: first, it can act on scalar fields by a formal scalar multiplication—to give a vector field called the gradient; second, it can act on vector fields by a formal dot product—to give a scalar field called the divergence; and lastly, it can act on vector fields by a formal cross product—to give a vector field called the curl. These formal products do not necessarily commute with other operators or products. These three uses are summarized as:

The total derivative of a generally vector-valued function ${\textstyle f\left(\mathbf {x} \right)}$ with a vector ${\textstyle \mathbf {x} }$ is its Jacobian matrix, ${\textstyle \mathbf {J_{f}} }$, whose entries are first-order partial derivatives of each component of ${\textstyle f}$ with respect to each coordinate of ${\textstyle \mathbf {x} }$. If ${\textstyle \mathbf {x} }$ has a dependency to another vector, let say ${\displaystyle \mathbf {x} \left(\mathbf {u} \right)}$, then the total derivative can be expanded to a matrix multiplication ${\textstyle \mathbf {J_{f}} \cdot \mathbf {J_{\mathbf {x} }} }$, where ${\textstyle \mathbf {J_{\mathbf {x} }} }$ is the Jacobian matrix of ${\textstyle \mathbf {x} }$, consisting of first-order partial derivatives of each component of ${\textstyle \mathbf {x} }$ with respect to each coordinate of ${\textstyle \mathbf {u} }$. If ${\textstyle \mathbf {u} }$ also has a dependency, let say ${\textstyle \mathbf {u} \left(\mathbf {p} \right)}$, then further expansion is possible in a similar manner; ${\textstyle \mathbf {J_{f}} \cdot \mathbf {J_{\mathbf {x} }} \cdot \mathbf {J_{\mathbf {u} }} }$. As a simple case, when ${\textstyle \mathbf {x} =\mathbf {x} \left(t\right)}$, it becomes ${\textstyle \mathbf {J_{f}} \cdot {\frac {d\mathbf {x} }{dt}}}$. All these expressions of the total derivative give the same meaning; it is the slope at a given point.