Method of characteristics

Short description: Technique for solving hyperbolic partial differential equations

Differential equations

Navier–Stokes differential equations used to simulate airflow around an obstruction.

Scope

* Natural sciences
* Engineering

* Astronomy
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* Chemistry

Biology
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Applied mathematics

* Continuum mechanics
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Classification

Types

* Ordinary
* Partial
* Differential-algebraic
* Integro-differential
* Fractional
* Linear
* Non-linear

By variable type

* Dependent and independent variables

* Autonomous
* Complex
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Features

* Order
* Operator

* Notation

Relation to processes

* Difference (discrete analogue)

* Stochastic
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* Delay

Solution

General topics

* Picard–Lindelöf theorem
* Wronskian

* Phase portrait
* Phase space

* Lyapunov / Asymptotic / Exponential stability
* Rate of convergence
* Series / Integral solutions

* Numerical integration
* Dirac delta function

Solution methods

* Inspection
* Method of characteristics

Euler
* Exponential response formula
* Finite difference (Crank–Nicolson)
* Finite element
* Infinite element
* Finite volume
* Galerkin
* Petrov–Galerkin
* Integrating factor
* Integral transforms
* Perturbation theory
* Runge–Kutta

* Separation of variables
* Undetermined coefficients
* Variation of parameters

People

* Isaac Newton
* Gottfried Leibniz
* Leonhard Euler
* Émile Picard
* Józef Maria Hoene-Wroński
* Ernst Lindelöf
* Rudolf Lipschitz
* Augustin-Louis Cauchy
* John Crank
* Phyllis Nicolson
* Carl David Tolmé Runge
* Martin Kutta

* v
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* e

In mathematics, the method of characteristics is a technique for solving partial differential equations. Typically, it applies to first-order equations, although more generally the method of characteristics is valid for any hyperbolic partial differential equation. The method is to reduce a partial differential equation to a family of ordinary differential equations along which the solution can be integrated from some initial data given on a suitable hypersurface.

## Contents

* 1 Characteristics of first-order partial differential equation
* 1.1 Linear and quasilinear cases
* 1.1.1 Proof for quasilinear Case
* 1.2 Fully nonlinear case
* 2 Example
* 3 Characteristics of linear differential operators
* 4 Qualitative analysis of characteristics
* 5 See also
* 6 Notes
* 7 References
* 8 External links

## Characteristics of first-order partial differential equation

For a first-order PDE (partial differential equation), the method of characteristics discovers curves (called characteristic curves or just characteristics) along which the PDE becomes an ordinary differential equation (ODE).[1] Once the ODE is found, it can be solved along the characteristic curves and transformed into a solution for the original PDE.

For the sake of simplicity, we confine our attention to the case of a function of two independent variables x and y for the moment. Consider a quasilinear PDE of the form

[math]\displaystyle{ a(x,y,z) \frac{\partial z}{\partial x}+b(x,y,z) \frac{\partial z}{\partial y}=c(x,y,z). }[/math]

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(1)

Suppose that a solution z is known, and consider the surface graph z = z(x,y) in R3. A normal vector to this surface is given by

[math]\displaystyle{ \left(\frac{\partial z}{\partial x}(x,y),\frac{\partial z}{\partial y}(x,y),-1\right).\, }[/math]

As a result,[2] equation (1) is equivalent to the geometrical statement that the vector field

[math]\displaystyle{ (a(x,y,z),b(x,y,z),c(x,y,z))\, }[/math]

is tangent to the surface z = z(x,y) at every point, for the dot product of this vector field with the above normal vector is zero. In other words, the graph of the solution must be a union of integral curves of this vector field. These integral curves are called the characteristic curves of the original partial differential equation and are given by the Lagrange–Charpit equations[3]

[math]\displaystyle{ \begin{array}{rcl} \frac{dx}{dt}&=&a(x,y,z),\\\ \frac{dy}{dt}&=&b(x,y,z),\\\ \frac{dz}{dt}&=&c(x,y,z). \end{array} }[/math]

A parametrization invariant form of the Lagrange–Charpit equations[3] is:

[math]\displaystyle{ \frac{dx}{a(x,y,z)} = \frac{dy}{b(x,y,z)} = \frac{dz}{c(x,y,z)} . }[/math]

### Linear and quasilinear cases

Consider now a PDE of the form

[math]\displaystyle{ \sum_{i=1}^n a_i(x_1,\dots,x_n,u) \frac{\partial u}{\partial x_i}=c(x_1,\dots,x_n,u). }[/math]

For this PDE to be linear, the coefficients ai may be functions of the spatial variables only, and independent of u. For it to be quasilinear,[4] ai may also depend on the value of the function, but not on any derivatives. The distinction between these two cases is inessential for the discussion here.

For a linear or quasilinear PDE, the characteristic curves are given parametrically by

[math]\displaystyle{ (x_1,\dots,x_n,u) = (x_1(s),\dots,x_n(s),u(s)) }[/math]
[math]\displaystyle{ u(\mathbf{X}(s)) = U(s) }[/math]

such that the following system of ODEs is satisfied

[math]\displaystyle{ \frac{dx_i}{ds} = a_i(x_1,\dots,x_n,u) }[/math]

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(2)

[math]\displaystyle{ \frac{du}{ds} = c(x_1,\dots,x_n,u). }[/math]

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(3)

Equations (2) and (3) give the characteristics of the PDE.

#### Proof for quasilinear Case

In the quasilinear case, the use of the method of characteristics is justified by Grönwall's inequality. The above equation may be written as [math]\displaystyle{ \mathbf{a}(\mathbf{x},u) \cdot \nabla u(\mathbf{x}) = c(\mathbf{x},u) }[/math]

We must distinguish between the solutions to the ODE and the solutions to the PDE, which we do not know are equal a priori. Letting capital letters be the solutions to the ODE we find [math]\displaystyle{ \mathbf{X}'(s) = \mathbf{a}(\mathbf{X}(s),U(s)) }[/math] [math]\displaystyle{ U'(s) = c(\mathbf{X}(s), U(s)) }[/math]

Examining [math]\displaystyle{ \Delta(s) = |u(\mathbf{X}(s)) - U(s)|^2 }[/math], we find, upon differentiating that [math]\displaystyle{ \Delta'(s) = 2\big(u(\mathbf{X}(s)) - U(s)\big) \Big(\mathbf{X}'(s)\cdot \nabla u(\mathbf{X}(s)) - U'(s)\Big) }[/math] which is the same as [math]\displaystyle{ \Delta'(s) = 2\big(u(\mathbf{X}(s)) - U(s)\big) \Big(\mathbf{a}(\mathbf{X}(s),U(s))\cdot \nabla u(\mathbf{X}(s)) - c(\mathbf{X}(s),U(s))\Big) }[/math]

We cannot conclude the above is 0 as we would like, since the PDE only guarantees us that this relationship is satisfied for [math]\displaystyle{ u(\mathbf{x}) }[/math], [math]\displaystyle{ \mathbf{a}(\mathbf{x},u) \cdot \nabla u(\mathbf{x}) = c(\mathbf{x},u) }[/math], and we do not yet know that [math]\displaystyle{ U(s) = u(\mathbf{X}(s)) }[/math].

However, we can see that [math]\displaystyle{ \Delta'(s) = 2\big(u(\mathbf{X}(s)) - U(s)\big) \Big(\mathbf{a}(\mathbf{X}(s),U(s))\cdot \nabla u(\mathbf{X}(s)) - c(\mathbf{X}(s),U(s))-\big(\mathbf{a}(\mathbf{X}(s),u(\mathbf{X}(s))) \cdot \nabla u(\mathbf{X}(s)) - c(\mathbf{X}(s),u(\mathbf{X}(s)))\big)\Big) }[/math] since by the PDE, the last term is 0. This equals [math]\displaystyle{ \Delta'(s) = 2\big(u(\mathbf{X}(s)) - U(s)\big) \Big(\big(\mathbf{a}(\mathbf{X}(s),U(s))-\mathbf{a}(\mathbf{X}(s),u(\mathbf{X}(s)))\big)\cdot \nabla u(\mathbf{X}(s)) - \big(c(\mathbf{X}(s),U(s))-c(\mathbf{X}(s),u(\mathbf{X}(s)))\big)\Big) }[/math]

By the triangle inequality, we have [math]\displaystyle{ |\Delta'(s)| \leq 2\big|u(\mathbf{X}(s)) - U(s)\big| \Big(\big\|\mathbf{a}(\mathbf{X}(s),U(s))-\mathbf{a}(\mathbf{X}(s),u(\mathbf{X}(s)))\big\| \ \|\nabla u(\mathbf{X}(s))\| + \big|c(\mathbf{X}(s),U(s))-c(\mathbf{X}(s),u(\mathbf{X}(s)))\big|\Big) }[/math]

Assuming [math]\displaystyle{ \mathbf{a},c }[/math] are at least [math]\displaystyle{ C^1 }[/math], we can bound this for small times. Choose a neighborhood [math]\displaystyle{ \Omega }[/math] around [math]\displaystyle{ \mathbf{X}(0), U(0) }[/math] small enough such that [math]\displaystyle{ \mathbf{a},c }[/math] are locally Lipschitz. By continuity, [math]\displaystyle{ (\mathbf{X}(s),U(s)) }[/math] will remain in [math]\displaystyle{ \Omega }[/math] for small enough [math]\displaystyle{ s }[/math]. Since [math]\displaystyle{ U(0) = u(\mathbf{X}(0)) }[/math], we also have that [math]\displaystyle{ (\mathbf{X}(s), u(\mathbf{X}(s))) }[/math] will be in [math]\displaystyle{ \Omega }[/math] for small enough [math]\displaystyle{ s }[/math] by continuity. So, [math]\displaystyle{ (\mathbf{X}(s),U(s)) \in \Omega }[/math] and [math]\displaystyle{ (\mathbf{X}(s), u(\mathbf{X}(s))) \in \Omega }[/math] for [math]\displaystyle{ s \in [0,s_0] }[/math]. Additionally, [math]\displaystyle{ \|\nabla u(\mathbf{X}(s))\| \leq M }[/math] for some [math]\displaystyle{ M \in \R }[/math] for [math]\displaystyle{ s \in [0,s_0] }[/math] by compactness. From this, we find the above is bounded as [math]\displaystyle{ |\Delta'(s)| \leq C|u(\mathbf{X}(s)) - U(s)|^2 = C |\Delta(s)| }[/math] for some [math]\displaystyle{ C \in \mathbb{R} }[/math]. It is a straightforward application of Grönwall's Inequality to show that since [math]\displaystyle{ \Delta(0) = 0 }[/math] we have [math]\displaystyle{ \Delta(s) = 0 }[/math] for as long as this inequality holds. We have some interval [math]\displaystyle{ [0, \epsilon) }[/math] such that [math]\displaystyle{ u(X(s)) = U(s) }[/math] in this interval. Choose the largest [math]\displaystyle{ \epsilon }[/math] such that this is true. Then, by continuity, [math]\displaystyle{ U(\epsilon) = u(\mathbf{X}(\epsilon)) }[/math]. Provided the ODE still has a solution in some interval after [math]\displaystyle{ \epsilon }[/math], we can repeat the argument above to find that [math]\displaystyle{ u(X(s)) = U(s) }[/math] in a larger interval. Thus, so long as the ODE has a solution, we have [math]\displaystyle{ u(X(s)) = U(s) }[/math].

### Fully nonlinear case

Consider the partial differential equation

[math]\displaystyle{ F(x_1,\dots,x_n,u,p_1,\dots,p_n)=0 }[/math]

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(4)

where the variables pi are shorthand for the partial derivatives

[math]\displaystyle{ p_i = \frac{\partial u}{\partial x_i}. }[/math]

Let (xi(s),u(s),pi(s)) be a curve in R2n+1. Suppose that u is any solution, and that

[math]\displaystyle{ u(s) = u(x_1(s),\dots,x_n(s)). }[/math]

Along a solution, differentiating (4) with respect to s gives

[math]\displaystyle{ \sum_i(F_{x_i} + F_u p_i)\dot{x}_i + \sum_i F_{p_i}\dot{p}_i = 0 }[/math]

[math]\displaystyle{ \dot{u} - \sum_i p_i \dot{x}_i = 0 }[/math]

[math]\displaystyle{ \sum_i (\dot{x}_i dp_i - \dot{p}_i dx_i)= 0. }[/math]

The second equation follows from applying the chain rule to a solution u, and the third follows by taking an exterior derivative of the relation [math]\displaystyle{ du - \sum_i p_i \, dx_i = 0 }[/math]. Manipulating these equations gives

[math]\displaystyle{ \dot{x}_i=\lambda F_{p_i},\quad\dot{p}_i=-\lambda(F_{x_i}+F_up_i),\quad \dot{u}=\lambda\sum_i p_iF_{p_i} }[/math]

where λ is a constant. Writing these equations more symmetrically, one obtains the Lagrange–Charpit equations for the characteristic

[math]\displaystyle{ \frac{\dot{x}_i}{F_{p_i}}=-\frac{\dot{p}_i}{F_{x_i}+F_up_i}=\frac{\dot{u}}{\sum p_iF_{p_i}}. }[/math]

Geometrically, the method of characteristics in the fully nonlinear case can be interpreted as requiring that the Monge cone of the differential equation should everywhere be tangent to the graph of the solution. The second order partial differential equation is solved with Charpit method .

## Example

As an example, consider the advection equation (this example assumes familiarity with PDE notation, and solutions to basic ODEs).

[math]\displaystyle{ a \frac{\partial u}{\partial x} + \frac{\partial u}{\partial t} = 0 }[/math]

where [math]\displaystyle{ a }[/math] is constant and [math]\displaystyle{ u }[/math] is a function of [math]\displaystyle{ x }[/math] and [math]\displaystyle{ t }[/math]. We want to transform this linear first-order PDE into an ODE along the appropriate curve; i.e. something of the form

[math]\displaystyle{ \frac{d}{ds}u(x(s), t(s)) = F(u, x(s), t(s)) , }[/math]

where [math]\displaystyle{ (x(s),t(s)) }[/math] is a characteristic line. First, we find

[math]\displaystyle{ \frac{d}{ds}u(x(s), t(s)) = \frac{\partial u}{\partial x} \frac{dx}{ds} + \frac{\partial u}{\partial t} \frac{dt}{ds} }[/math]

by the chain rule. Now, if we set [math]\displaystyle{ \frac{dx}{ds} = a }[/math] and [math]\displaystyle{ \frac{dt}{ds} = 1 }[/math] we get

[math]\displaystyle{ a \frac{\partial u}{\partial x} + \frac{\partial u}{\partial t} }[/math]

which is the left hand side of the PDE we started with. Thus

[math]\displaystyle{ \frac{d}{ds}u = a \frac{\partial u}{\partial x} + \frac{\partial u}{\partial t} = 0. }[/math]

So, along the characteristic line [math]\displaystyle{ (x(s), t(s)) }[/math], the original PDE becomes the ODE [math]\displaystyle{ u_s = F(u, x(s), t(s)) = 0 }[/math]. That is to say that along the characteristics, the solution is constant. Thus, [math]\displaystyle{ u(x_s, t_s) = u(x_0, 0) }[/math] where [math]\displaystyle{ (x_s, t_s)\, }[/math] and [math]\displaystyle{ (x_0, 0) }[/math] lie on the same characteristic. Therefore, to determine the general solution, it is enough to find the characteristics by solving the characteristic system of ODEs:

* [math]\displaystyle{ \frac{dt}{ds} = 1 }[/math], letting [math]\displaystyle{ t(0)=0 }[/math] we know [math]\displaystyle{ t=s }[/math],
* [math]\displaystyle{ \frac{dx}{ds} = a }[/math], letting [math]\displaystyle{ x(0)=x_0 }[/math] we know [math]\displaystyle{ x=as+x_0=at+x_0 }[/math],
* [math]\displaystyle{ \frac{du}{ds} = 0 }[/math], letting [math]\displaystyle{ u(0)=f(x_0) }[/math] we know [math]\displaystyle{ u(x(t), t)=f(x_0)=f(x-at) }[/math].

In this case, the characteristic lines are straight lines with slope [math]\displaystyle{ a }[/math], and the value of [math]\displaystyle{ u }[/math] remains constant along any characteristic line.

## Characteristics of linear differential operators

Let X be a differentiable manifold and P a linear differential operator

[math]\displaystyle{ P : C^\infty(X) \to C^\infty(X) }[/math]

of order k. In a local coordinate system xi,

[math]\displaystyle{ P = \sum_{|\alpha|\le k} P^{\alpha}(x)\frac{\partial}{\partial x^\alpha} }[/math]

in which α denotes a multi-index. The principal symbol of P, denoted σP, is the function on the cotangent bundle T∗X defined in these local coordinates by

[math]\displaystyle{ \sigma_P(x,\xi) = \sum_{|\alpha|=k} P^\alpha(x)\xi_\alpha }[/math]

where the ξi are the fiber coordinates on the cotangent bundle induced by the coordinate differentials dxi. Although this is defined using a particular coordinate system, the transformation law relating the ξi and the xi ensures that σP is a well-defined function on the cotangent bundle.

The function σP is homogeneous of degree k in the ξ variable. The zeros of σP, away from the zero section of T∗X, are the characteristics of P. A hypersurface of X defined by the equation F(x) = c is called a characteristic hypersurface at x if

[math]\displaystyle{ \sigma_P(x,dF(x)) = 0. }[/math]

Invariantly, a characteristic hypersurface is a hypersurface whose conormal bundle is in the characteristic set of P.

## Qualitative analysis of characteristics

Characteristics are also a powerful tool for gaining qualitative insight into a PDE.

One can use the crossings of the characteristics to find shock waves for potential flow in a compressible fluid. Intuitively, we can think of each characteristic line implying a solution to [math]\displaystyle{ u }[/math] along itself. Thus, when two characteristics cross, the function becomes multi-valued resulting in a non-physical solution. Physically, this contradiction is removed by the formation of a shock wave, a tangential discontinuity or a weak discontinuity and can result in non-potential flow, violating the initial assumptions.[5]

Characteristics may fail to cover part of the domain of the PDE. This is called a rarefaction, and indicates the solution typically exists only in a weak, i.e. integral equation, sense.

The direction of the characteristic lines indicates the flow of values through the solution, as the example above demonstrates. This kind of knowledge is useful when solving PDEs numerically as it can indicate which finite difference scheme is best for the problem.

## See also

* Method of quantum characteristics

## Notes

1. ↑ Zachmanoglou, E. C.; Thoe, Dale W. (1976), "Linear Partial Differential Equations : Characteristics, Classification, and Canonical Forms", Introduction to Partial Differential Equations with Applications, Baltimore: Williams & Wilkins, pp. 112–152, ISBN 0-486-65251-3
2. ↑ John, Fritz (1991), Partial differential equations (4th ed.), Springer, ISBN 978-0-387-90609-6, https://archive.org/details/partialdifferent00john_0
3. ↑ 3.0 3.1 Delgado, Manuel (1997), "The Lagrange-Charpit Method", SIAM Review 39 (2): 298–304, doi:10.1137/S0036144595293534, Bibcode: 1997SIAMR..39..298D
4. ↑ "Partial Differential Equations (PDEs)—Wolfram Language Documentation". https://reference.wolfram.com/language/tutorial/DSolveLinearAndQuasiLinearFirstOrderPDEs.html.
5. ↑ Debnath, Lokenath (2005), "Conservation Laws and Shock Waves", Nonlinear Partial Differential Equations for Scientists and Engineers (2nd ed.), Boston: Birkhäuser, pp. 251–276, ISBN 0-8176-4323-0

## References

* Courant, Richard; Hilbert, David (1962), Methods of Mathematical Physics, Volume II, Wiley-Interscience
* Evans, Lawrence C. (1998), Partial Differential Equations, Providence: American Mathematical Society, ISBN 0-8218-0772-2
* Polyanin, A. D.; Zaitsev, V. F.; Moussiaux, A. (2002), Handbook of First Order Partial Differential Equations, London: Taylor & Francis, ISBN 0-415-27267-X
* Polyanin, A. D. (2002), Handbook of Linear Partial Differential Equations for Engineers and Scientists, Boca Raton: Chapman & Hall/CRC Press, ISBN 1-58488-299-9
* Sarra, Scott (2003), "The Method of Characteristics with applications to Conservation Laws", Journal of Online Mathematics and Its Applications, http://www.scottsarra.org/shock/shock.html .
* Streeter, VL; Wylie, EB (1998), Fluid mechanics (International 9th Revised ed.), McGraw-Hill Higher Education

## External links

* Prof. Scott Sarra tutorial on Method of Characteristics
* Prof. Alan Hood tutorial on Method of Characteristics

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Numerical partial differential equations by method

Finite difference|

| Parabolic|

* Forward-time central-space (FTCS)
* Crank–Nicolson

|

Hyperbolic|

* Lax–Friedrichs
* Lax–Wendroff
* MacCormack
* Upwind
* Method of characteristics

Others|

* Alternating direction-implicit (ADI)
* Finite-difference time-domain (FDTD)

Finite volume|

* Godunov
* High-resolution
* Monotonic upstream-centered (MUSCL)
* Advection upstream-splitting (AUSM)
* Riemann solver
* essentially non-oscillatory (ENO)
* weighted essentially non-oscillatory (WENO)

Finite element|

* hp-FEM
* Extended (XFEM)
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* Loubignac iteration
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Meshless/Meshfree|

* Smoothed-particle hydrodynamics (SPH)
* Moving particle semi-implicit method (MPS)
* Material point method (MPM)
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Domain decomposition|

* Schur complement
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* additive
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* Neumann–Dirichlet
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* Balancing (BDD)
* Balancing by constraints (BDDC)
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* FETI-DP

Others|

* Spectral
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* Method of lines
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* Isogeometric analysis
* Infinite difference method
* Infinite element method
* Galerkin method
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* Validated numerics
* Computer-assisted proof

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