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| {{Distinguish2|"finite difference method based on variation principle", the first name of [[finite element method]]{{citation needed|date=August 2013}}}}
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| {{Differential equations}}
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| In [[mathematics]], '''finite-difference methods''' (FDM) are [[numerical methods]] for approximating the solutions to [[differential equations]] using [[finite difference]] equations to approximate derivatives.
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| == Derivation from Taylor's polynomial ==
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| First, assuming the function whose derivatives are to be approximated is properly-behaved, by [[Taylor's theorem]], we can create a [[Taylor Series]] expansion
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| :<math>f(x_0 + h) = f(x_0) + \frac{f'(x_0)}{1!}h + \frac{f^{(2)}(x_0)}{2!}h^2 + \cdots + \frac{f^{(n)}(x_0)}{n!}h^n + R_n(x),</math>
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| where ''n''! denotes the [[factorial]] of ''n'', and ''R''<sub>''n''</sub>(''x'') is a remainder term, denoting the difference between the Taylor polynomial of degree ''n'' and the original function. We will derive an approximation for the first derivative of the function "f" by first truncating the Taylor polynomial:
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| :<math>f(x_0 + h) = f(x_0) + f'(x_0)h + R_1(x),</math>
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| Setting, x<sub>0</sub>=a and (x-a)=h we have,
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| :<math>f(a+h) = f(a) + f'(a)h + R_1(x),</math>
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| Dividing across by ''h'' gives:
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| :<math>{f(a+h)\over h} = {f(a)\over h} + f'(a)+{R_1(x)\over h} </math>
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| Solving for f'(a):
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| :<math>f'(a) = {f(a+h)-f(a)\over h} - {R_1(x)\over h}</math>
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| Assuming that <math>R_1(x)</math> is sufficiently small, the approximation of the first derivative of "f" is:
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| :<math>f'(a)\approx {f(a+h)-f(a)\over h}.</math>
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| == Accuracy and order ==
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| {{see also|Finite difference coefficient}}
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| The error in a method's solution is defined as the difference between its approximation and the exact analytical solution. The two sources of error in finite difference methods are [[round-off error]], the loss of precision due to computer rounding of decimal quantities, and [[truncation error]] or [[discretization error]], the difference between the exact solution of the finite difference equation and the exact quantity assuming perfect arithmetic (that is, assuming no round-off).
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| [[File:Finite Differences.svg|right|thumb|The finite difference method relies on discretizing a function on a grid.]]
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| To use a finite difference method to attempt to solve (or, more generally, approximate the solution to) a problem, one must first discretize the problem's domain. This is usually done by dividing the domain into a uniform grid (see image to the right). Note that this means that finite-difference methods produce sets of discrete numerical approximations to the derivative, often in a "time-stepping" manner.
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| An expression of general interest is the [[local truncation error]] of a method. Typically expressed using [[Big-O notation]], local truncation error refers to the error from a single application of a method. That is, it is the quantity <math>f'(x_i) - f'_i</math> if <math>f'(x_i)</math> refers to the exact value and <math>f'_i</math> to the numerical approximation. The remainder term of a Taylor polynomial is convenient for analyzing the local truncation error. Using the Lagrange form of the remainder from the Taylor polynomial for <math>f(x_0 + h)</math>, which is
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| :<math>
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| R_n(x_0 + h) = \frac{f^{(n+1)}(\xi)}{(n+1)!} (h)^{n+1}
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| </math>, where <math> x_0 < \xi < x_0 + h</math>,
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| the dominant term of the local truncation error can be discovered. For example, again using the forward-difference formula for the first derivative, knowing that <math>f(x_i)=f(x_0+i h)</math>,
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| :<math> f(x_0 + i h) = f(x_0) + f'(x_0)i h + \frac{f''(\xi)}{2!} (i h)^{2}, </math>
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| and with some algebraic manipulation, this leads to
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| :<math> \frac{f(x_0 + i h) - f(x_0)}{i h} = f'(x_0) + \frac{f''(\xi)}{2!} i h, </math>
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| and further noting that the quantity on the left is the approximation from the finite difference method and that the quantity on the right is the exact quantity of interest plus a remainder, clearly that remainder is the local truncation error. A final expression of this example and its order is:
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| :<math> \frac{f(x_0 + i h) - f(x_0)}{i h} = f'(x_0) + O(h). </math>
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| This means that, in this case, the local truncation error is proportional to the step size.
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| == Example: ordinary differential equation ==
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| For example, consider the ordinary differential equation
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| :<math> u'(x) = 3u(x) + 2. \, </math>
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| The [[Numerical ordinary differential equations#The Euler method|Euler method]] for solving this equation uses the finite difference quotient
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| :<math>\frac{u(x+h) - u(x)}{h} \approx u'(x)</math>
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| to approximate the differential equation by first substituting in for u'(x) and applying a little algebra to get
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| :<math> u(x+h) = u(x) + h(3u(x)+2). \, </math>
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| The last equation is a finite-difference equation, and solving this equation gives an approximate solution to the differential equation.
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| == Example: The heat equation ==
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| Consider the normalized [[heat equation]] in one dimension, with homogeneous [[Dirichlet boundary condition]]s
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| :<math> U_t=U_{xx} \, </math>
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| :<math> U(0,t)=U(1,t)=0 \, </math> (boundary condition)
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| :<math> U(x,0) =U_0(x) \, </math> (initial condition)
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| One way to numerically solve this equation is to approximate all the derivatives by finite differences. We partition the domain in space using a mesh <math> x_0, ..., x_J </math> and in time using a mesh <math> t_0, ...., t_N </math>. We assume a uniform partition both in space and in time, so the difference between two consecutive space points will be ''h'' and between two consecutive time points will be ''k''. The points
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| :<math> u(x_j,t_n) = u_{j}^n </math>
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| will represent the numerical approximation of <math> u(x_j, t_n). </math>
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| ===Explicit method===
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| [[File:Explicit method-stencil.svg|right|thumb|The [[Stencil (numerical analysis)|stencil]] for the most common explicit method for the heat equation.]]
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| Using a [[forward difference]] '''at time <math> t_n </math>''' and a second-order [[central difference]] for the space derivative at position <math> x_j </math> ([[FTCS scheme|FTCS]]) we get the recurrence equation:
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| :<math> \frac{u_{j}^{n+1} - u_{j}^{n}}{k} = \frac{u_{j+1}^n - 2u_{j}^n + u_{j-1}^n}{h^2}. \, </math>
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| This is an [[explicit method]] for solving the one-dimensional [[heat equation]].
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| We can obtain <math> u_j^{n+1} </math> from the other values this way:
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| :<math> u_{j}^{n+1} = (1-2r)u_{j}^{n} + ru_{j-1}^{n} + ru_{j+1}^{n} </math> | |
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| where <math> r=k/h^2. </math>
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| So, with this recurrence relation, and knowing the values at time ''n'', one can obtain the corresponding values at time ''n''+1. <math> u_0^n </math> and <math> u_J^n </math> must be replaced by the boundary conditions, in this example they are both 0.
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| This explicit method is known to be [[numerically stable]] and [[limit of a sequence|convergent]] whenever <math> r\le 1/2 </math>.<ref>Crank, J. ''The Mathematics of Diffusion''. 2nd Edition, Oxford, 1975, p. 143.</ref> The numerical errors are proportional to the time step and the square of the space step:
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| :<math> \Delta u = O(k)+O(h^2) \, </math>
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| ===Implicit method===
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| [[File:Implicit method-stencil.svg|right|thumb|The implicit method stencil.]]
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| If we use the [[backward difference]] '''at time <math> t_{n+1} </math>''' and a second-order central difference for the space derivative at position <math> x_j </math> (The Backward Time, Centered Space Method "BTCS") we get the recurrence equation:
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| :<math> \frac{u_{j}^{n+1} - u_{j}^{n}}{k} =\frac{u_{j+1}^{n+1} - 2u_{j}^{n+1} + u_{j-1}^{n+1}}{h^2}. \, </math>
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| This is an [[implicit method]] for solving the one-dimensional [[heat equation]].
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| We can obtain <math> u_j^{n+1} </math> from solving a system of linear equations:
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| :<math> (1+2r)u_j^{n+1} - ru_{j-1}^{n+1} - ru_{j+1}^{n+1}= u_{j}^{n} </math>
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| The scheme is always [[numerically stable]] and convergent but usually more numerically intensive than the explicit method as it requires solving a system of numerical equations on each time step. The errors are linear over the time step and quadratic over the space step:
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| :<math> \Delta u = O(k)+O(h^2). \, </math>
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| ===Crank–Nicolson method===
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| Finally if we use the central difference at time <math> t_{n+1/2} </math> and a second-order central difference for the space derivative at position <math> x_j </math> ("CTCS") we get the recurrence equation:
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| :<math> \frac{u_j^{n+1} - u_j^{n}}{k} = \frac{1}{2} \left(\frac{u_{j+1}^{n+1} - 2u_j^{n+1} + u_{j-1}^{n+1}}{h^2}+\frac{u_{j+1}^{n} - 2u_j^{n} + u_{j-1}^{n}}{h^2}\right).\, </math>
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| This formula is known as the [[Crank–Nicolson method]].
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| [[File:Crank-Nicolson-stencil.svg|right|thumb|The Crank–Nicolson stencil.]]
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| We can obtain <math> u_j^{n+1} </math> from solving a system of linear equations:
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| :<math> (2+2r)u_j^{n+1} - ru_{j-1}^{n+1} - ru_{j+1}^{n+1}= (2-2r)u_j^n + ru_{j-1}^n + ru_{j+1}^n </math>
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| The scheme is always [[numerically stable]] and convergent but usually more numerically intensive as it requires solving a system of numerical equations on each time step. The errors are quadratic over both the time step and the space step:
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| :<math> \Delta u = O(k^2)+O(h^2). \, </math>
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| Usually the Crank–Nicolson scheme is the most accurate scheme for small time steps. The explicit scheme is the least accurate and can be unstable, but is also the easiest to implement and the least numerically intensive. The implicit scheme works the best for large time steps.
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| ==See also==
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| * [[Finite element method]]
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| * [[Finite difference]]
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| * [[Finite difference time domain]]
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| * [[Stencil (numerical analysis)]]
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| * [[Finite difference coefficients]]
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| * [[Five-point stencil]]
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| * [[Lax–Richtmyer theorem]]
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| * [[Finite difference methods for option pricing]]
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| * [[Upwind differencing scheme for convection]]
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| *[[Central differencing scheme]]
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| {{Numerical PDE}}
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| ==References==
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| {{Reflist}}
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| * K.W. Morton and D.F. Mayers, ''Numerical Solution of Partial Differential Equations, An Introduction''. Cambridge University Press, 2005.
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| * Oliver Rübenkönig, ''[http://www.imtek.de/simulation/mathematica/IMSweb/imsTOC/Lectures%20and%20Tips/Simulation%20I/FDM_introDocu.html The Finite Difference Method (FDM) - An introduction]'', (2006) [[Albert Ludwigs University of Freiburg]]
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| * Autar Kaw and E. Eric Kalu, ''Numerical Methods with Applications'', (2008) [http://www.autarkaw.com/books/numericalmethods/index.html]
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| ==External links==
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| *[http://math.fullerton.edu/mathews/n2003/finitediffpde/FiniteDifferencePDEBib/Links/FiniteDifferencePDEBib_lnk_1.html List of Internet Resources for the Finite Difference Method for PDEs]
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| * [http://numericalmethods.eng.usf.edu/topics/finite_difference_method.html Finite Difference Method of Solving ODEs (Boundary Value Problems) Notes, PPT, Maple, Mathcad, Matlab, Mathematica]
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| *[http://ltl.iams.sinica.edu.tw/document/training_lectures/2006/SH_Chen/Finite_Difference_Methods.pdf Lecture Notes] Shih-Hung Chen, [[National Central University]]
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| * [[Randall J. LeVeque]], ''[http://faculty.washington.edu/rjl/fdmbook/ Finite Difference Methods for Ordinary and Partial Differential Equations]'', SIAM, 2007.
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| *[http://www.adeptscience.co.uk/products/mathsim/maple/powertools/des/unit18.html#MapleAutoBookmark4 Finite Difference Method]
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| *[http://math.fullerton.edu/mathews/n2003/FiniteDifferenceMod.html Finite Difference Method for Boundary Value Problems]
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| *[http://www.composite-agency.com/product.htm Finite Difference Methodology in Materials Science]
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| {{DEFAULTSORT:Finite Difference Method}}
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| [[Category:Finite differences]]
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| [[Category:Numerical differential equations]]
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