ecterrab

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Exact solutions for PDE and Boundary / Initial Conditions

 

Significant developments happened during 2018 in Maple's ability for the exact solving of PDE with Boundary / Initial conditions. This is work in collaboration with Katherina von Bülow. Part of these developments were mentioned in previous posts.  The project is still active but it's December, time to summarize.

 

First of all thanks to all of you who provided feedback. The new functionality is described below, in 11 brief Sections, with 30 selected examples and a few comments. A worksheet with this contents is linked at the end of this post. Some of these improvements appeared first in 2018.1, then in 2018.2, but other ones are posterior. To reproduce the input/output below in Maple 2018.2.1, the latest Maplesoft Physics Updates (version 269 or higher) needs to be installed.

 

1. PDE and BC problems solved using linear change of variables

 

PDE and BC problems often require that the boundary and initial conditions be given at certain evaluation points (usually in which one of the variables is equal to zero). Using linear changes of variables, however, it is possible to change the evaluation points of BC, obtaining the solution for the new variables, and then changing back to the original variables. This is now automatically done by the pdsolve command.

 

Example 1: A heat PDE & BC problem in a semi-infinite domain:

> 

pde__1 := diff(u(x, t), t) = (1/4)*(diff(u(x, t), x, x))

> 

iv__1 := u(-A, t) = 0, u(x, B) = 10

 

Note the evaluation points A and B. The method typically described in textbooks requires the evaluation points to be A = 0, B = 0. The change of variables automatically used in this case is:

> 

transformation := {t = tau+B, x = xi-A, u(x, t) = upsilon(xi, tau)}

{t = tau+B, x = xi-A, u(x, t) = upsilon(xi, tau)}

(1)

so that pdsolve's task becomes solving this other problem, now with the appropriate evaluation points

> 

PDEtools:-dchange(transformation, [pde__1, iv__1], {tau, upsilon, xi})

[diff(upsilon(xi, tau), tau) = (1/4)*(diff(diff(upsilon(xi, tau), xi), xi)), upsilon(0, tau) = 0, upsilon(xi, 0) = 10]

(2)

and then changing the variables back to the original {x, t, u} and giving the solution. The process all in one go:

> 

`assuming`([pdsolve([pde__1, iv__1])], [abs(A) < x, abs(B) < t])

u(x, t) = 10*erf((x+A)/(t-B)^(1/2))

(3)

 

Example 2: A heat PDE with a source and a piecewise initial condition

> 

pde__2 := diff(u(x, t), t)+1 = mu*(diff(u(x, t), x, x))

> 

iv__2 := u(x, 1) = piecewise(0 <= x, 0, x < 0, 1)

> 

`assuming`([pdsolve([pde__2, iv__2])], [0 < mu, 0 < t])

u(x, t) = 3/2-(1/2)*erf((1/2)*x/(mu^(1/2)*(t-1)^(1/2)))-t

(4)

 

Example 3: A wave PDE & BC problem in a semi-infinite domain:

> 

pde__3 := diff(u(x, t), t, t) = diff(u(x, t), x, x)

> 

iv__3 := u(x, 1) = exp(-(x-6)^2)+exp(-(x+6)^2), (D[2](u))(x, 1) = 1/2

> 

`assuming`([pdsolve([pde__3, iv__3])], [0 < t])

u(x, t) = (1/2)*exp(-(-x+t+5)^2)+(1/2)*exp(-(-x+t-7)^2)+(1/2)*exp(-(x+t-7)^2)+(1/2)*exp(-(x+t+5)^2)+(1/2)*t-1/2

(5)

 

Example 4: A wave PDE & BC problem in a semi-infinite domain:

> 

pde__4 := diff(u(x, t), t, t)-(1/4)*(diff(u(x, t), x, x)) = 0

> 

iv__4 := (D[1](u))(1, t) = 0, u(x, 0) = exp(-x^2), (D[2](u))(x, 0) = 0

> 

`assuming`([pdsolve([pde__4, iv__4])], [1 < x, 0 < t])

u(x, t) = piecewise((1/2)*t < x-1, (1/2)*exp(-(1/4)*(t+2*x)^2)+(1/2)*exp(-(1/4)*(t-2*x)^2), x-1 < (1/2)*t, (1/2)*exp(-(1/4)*(t+2*x)^2)+(1/2)*exp(-(1/4)*(t-2*x+4)^2))

(6)

 

Example 5: A wave PDE with a source:

> 

pde__5 := diff(u(x, t), t, t)-c^2*(diff(u(x, t), x, x)) = f(x, t)

> 

iv__5 := u(x, 1) = g(x), (D[2](u))(x, 1) = h(x)

> 

pdsolve([pde__5, iv__5], u(x, t))

u(x, t) = (1/2)*(Int(Int((diff(diff(h(zeta), zeta), zeta))*c^2*tau+(diff(diff(g(zeta), zeta), zeta))*c^2+f(zeta, tau+1), zeta = (-t+tau+1)*c+x .. x+c*(t-1-tau)), tau = 0 .. t-1)+(2*t-2)*c*h(x)+2*g(x)*c)/c

(7)
> 

pdetest(u(x, t) = (1/2)*(Int(Int((diff(diff(h(zeta), zeta), zeta))*c^2*tau+(diff(diff(g(zeta), zeta), zeta))*c^2+f(zeta, tau+1), zeta = (-t+tau+1)*c+x .. x+c*(t-1-tau)), tau = 0 .. t-1)+(2*t-2)*c*h(x)+2*g(x)*c)/c, [pde__5, iv__5])

[0, 0, 0]

(8)

2. It is now possible to specify or exclude method(s) for solving

 

The pdsolve/BC solving methods can now be indicated, either to be used for solving, as in methods = [method__1, method__2, () .. ()] to be tried in the indicated order, or to be excluded, as in exclude = [method__1, method__2, () .. ()]. The methods and sub-methods available are organized in a table,
`pdsolve/BC/methods`

> 

indices(`pdsolve/BC/methods`)

[1], [2], [3], [2, "Series"], [2, "Heat"], ["high_order"], ["system"], [2, "Wave"], [2, "SpecializeArbitraryFunctions"]

(9)


So, for example, the methods for PDEs of first order and second order are, respectively,

> 

`pdsolve/BC/methods`[1]

["SpecializeArbitraryFunctions", "Fourier", "Laplace", "Generic", "PolynomialSolutions", "LinearDifferentialOperator"]

(10)
> 

`pdsolve/BC/methods`[2]

["SpecializeArbitraryFunctions", "SpecializeArbitraryConstants", "Wave", "Heat", "Series", "Laplace", "Fourier", "Generic", "PolynomialSolutions", "LinearDifferentialOperator", "Superposition"]

(11)

 

Some methods have sub-methods (their existence is visible in (9)):

> 

`pdsolve/BC/methods`[2, "Series"]

["ThreeBCsincos", "FourBC", "ThreeBC", "ThreeBCPeriodic", "WithSourceTerm", "ThreeVariables"]

(12)
> 

`pdsolve/BC/methods`[2, "Heat"]

["SemiInfiniteDomain", "WithSourceTerm"]

(13)

 

Example 6:

> 

pde__6 := diff(u(r, theta), r, r)+diff(u(r, theta), theta, theta) = 0

> 

iv__6 := u(2, theta) = 3*sin(2*theta)+1

> 

pdsolve([pde__6, iv__6])

u(r, theta) = -_F2(-I*r+2*I+theta)+1-3*sin((2*I)*r-4*I-2*theta)+_F2(I*r-2*I+theta)

(14)
> 

pdsolve([pde__6, iv__6], method = Fourier)

u(r, theta) = ((3/2)*I)*exp(2*r-4-(2*I)*theta)-((3/2)*I)*exp(-2*r+4+(2*I)*theta)+1

(15)

Example 7:

> 

pde__7 := diff(u(x, y), x, x)+diff(u(x, y), y, y) = 0

> 

iv__7 := u(x, 0) = Dirac(x)

> 

pdsolve([pde__7, iv__7])

u(x, y) = Dirac(x)-(1/2)*Dirac(2, x)*y^2+_C3*y

(16)
> 

pdsolve([pde__7, iv__7], method = Fourier)

u(x, y) = invfourier(exp(-s*y), s, x)

(17)
> 

convert(u(x, y) = invfourier(exp(-s*y), s, x), Int)

u(x, y) = (1/2)*(Int(exp(-s*y+I*s*x), s = -infinity .. infinity))/Pi

(18)
> 

pdsolve([pde__7, iv__7], method = Generic)

u(x, y) = -_F2(-y+I*x)+Dirac(x+I*y)+_F2(y+I*x)

(19)

3. Series solutions for linear PDE and BC problems solved via product separation with eigenvalues that are the roots of algebraic expressions which cannot be inverted

 

Linear problems for which the PDE can be separated by product, giving rise to Sturm-Liouville problems for the separation constant (eigenvalue) and separated functions (eigenfunctions), do not always result in solvable equations for the eigenvalues. Below are examples where the eigenvalues are respectively roots of a sum of  BesselJ functions and of the non-inversible equation tan(lambda[n])+lambda[n] = 0.

 

Example 8: This problem represents the temperature distribution in a thin circular plate whose lateral surfaces are insulated (Articolo example 6.9.2):

> 

pde__8 := diff(u(r, theta, t), t) = (diff(u(r, theta, t), r)+r*(diff(u(r, theta, t), r, r))+(diff(u(r, theta, t), theta, theta))/r)/(25*r)

> 

iv__8 := (D[1](u))(1, theta, t) = 0, u(r, 0, t) = 0, u(r, Pi, t) = 0, u(r, theta, 0) = (r-(1/3)*r^3)*sin(theta)

> 

pdsolve([pde__8, iv__8])

u(r, theta, t) = `casesplit/ans`(Sum(-(4/3)*BesselJ(1, lambda[n]*r)*sin(theta)*exp(-(1/25)*lambda[n]^2*t)*(BesselJ(0, lambda[n])*lambda[n]^3-BesselJ(1, lambda[n])*lambda[n]^2+4*lambda[n]*BesselJ(0, lambda[n])-8*BesselJ(1, lambda[n]))/(lambda[n]^3*(BesselJ(0, lambda[n])^2*lambda[n]+BesselJ(1, lambda[n])^2*lambda[n]-2*BesselJ(0, lambda[n])*BesselJ(1, lambda[n]))), n = 0 .. infinity), {And(-BesselJ(1, lambda[n])+BesselJ(2, lambda[n])*lambda[n] = 0, 0 < lambda[n])})

(20)

 

In the above we see that the eigenvalue `&lambda;__n` satisfies -BesselJ(1, lambda[n])+BesselJ(2, lambda[n])*lambda[n] = 0. When `&lambda;__n` is the root of one single BesselJ or BesselY function of integer order, the Maple functions BesselJZeros and BesselYZeros are used instead. That is the case, for instance, if we slightly modify this problem changing the first boundary condition to be u(1, theta, t) = 0 instead of (D[1](u))(1, theta, t) = 0

> 

`iv__8.1` := u(1, theta, t) = 0, u(r, 0, t) = 0, u(r, Pi, t) = 0, u(r, theta, 0) = (r-(1/3)*r^3)*sin(theta)

> 

pdsolve([pde__8, `iv__8.1`])

u(r, theta, t) = `casesplit/ans`(Sum(-(4/3)*BesselJ(1, lambda[n]*r)*sin(theta)*exp(-(1/25)*lambda[n]^2*t)*(lambda[n]^2+4)/(BesselJ(0, lambda[n])*lambda[n]^3), n = 1 .. infinity), {And(lambda[n] = BesselJZeros(1, n), 0 < lambda[n])})

(21)

Example 9: This problem represents the temperature distribution in a thin rod whose left end is held at a fixed temperature of 5 and whose right end loses heat by convection into a medium whose temperature is 10. There is also an internal heat source term in the PDE (Articolo's textbook, example 8.4.3):

> 

pde__9 := diff(u(x, t), t) = (1/20)*(diff(u(x, t), x, x))+t

> 

iv__9 := u(0, t) = 5, u(1, t)+(D[1](u))(1, t) = 10, u(x, 0) = -40*x^2*(1/3)+45*x*(1/2)+5

> 

pdsolve([pde__9, iv__9], u(x, t))

u(x, t) = `casesplit/ans`(Sum(piecewise(lambda[n] = 0, 0, (80/3)*exp(-(1/20)*lambda[n]^2*t)*sin(lambda[n]*x)*(lambda[n]^2*cos(lambda[n])+lambda[n]*sin(lambda[n])+4*cos(lambda[n])-4)/(lambda[n]^2*(sin(2*lambda[n])-2*lambda[n]))), n = 0 .. infinity)+Int(Sum(piecewise(lambda[n] = 0, 0, 4*exp(-(1/20)*lambda[n]^2*(t-tau))*sin(lambda[n]*x)*tau*(cos(lambda[n])-1)/(sin(2*lambda[n])-2*lambda[n])), n = 0 .. infinity), tau = 0 .. t)+(5/2)*x+5, {And(tan(lambda[n])+lambda[n] = 0, 0 < lambda[n])})

(22)

For information on how to test or plot a solution like the one above, please see the end of the Mapleprimes post "Sturm-Liouville problem with eigenvalues that are the roots of algebraic expressions which cannot be inverted" 

 

4. Superposition method for linear PDE with more than one non-homogeneous BC

 

Previously, for linear homogeneous PDE problems with non-periodic initial and boundary conditions, pdsolve was only consistently able to solve the problem as long as at most one of those conditions was non-homogeneous. The superposition method works by taking advantage of the linearity of the problem and the fact that the solution to such a problem in which two or more of the BC are non-homogeneous can be given as

u = u__1+u__2 + ...,  where each u__i is a solution of the PDE with all but one of the BC homogenized.

 

Example 10: A Laplace PDE with one homogeneous and three non-homogeneous conditions:

> 

pde__10 := diff(u(x, y), x, x)+diff(u(x, y), y, y) = 0

> 

iv__10 := u(0, y) = 0, u(Pi, y) = sinh(Pi)*cos(y), u(x, 0) = sin(x), u(x, Pi) = -sinh(x)

> 

pdsolve([pde__10, iv__10])

u(x, y) = ((exp(2*Pi)-1)*(Sum((-1)^n*n*(exp(2*Pi)-1)*exp(n*(Pi-y)-Pi)*sin(n*x)*(exp(2*n*y)-1)/(Pi*(n^2+1)*(exp(2*Pi*n)-1)), n = 1 .. infinity))+(exp(2*Pi)-1)*(Sum(2*sin(n*y)*exp(n*(Pi-x))*n*sinh(Pi)*((-1)^n+1)*(exp(2*n*x)-1)/(Pi*(exp(2*Pi*n)-1)*(n^2-1)), n = 2 .. infinity))+sin(x)*(exp(-y+2*Pi)-exp(y)))/(exp(2*Pi)-1)

(23)

 

5. Polynomial solutions method:

 

This new method gives pdsolve better performance when the PDE & BC problems admit polynomial solutions.

 

Example 11:

> 

pde__11 := diff(u(x, y), x, x)+y*(diff(u(x, y), y, y)) = 0

> 

iv__11 := u(x, 0) = 0, (D[2](u))(x, 0) = x^2

> 

pdsolve([pde__11, iv__11], u(x, y))

u(x, y) = y*(x^2-y)

(24)

 

6. Solving more problems using the Laplace transform or the Fourier transform

 

These methods now solve more problems and are no longer restricted to PDE of first or second order.

 

Example 12: A third order PDE & BC problem:

> 

pde__12 := diff(u(x, t), t) = -(diff(u(x, t), x, x, x))

> 

iv__12 := u(x, 0) = f(x)

> 

pdsolve([pde__12, iv__12])

u(x, t) = (1/4)*(Int((4/3)*Pi*f(-zeta)*(-(x+zeta)/(-t)^(1/3))^(1/2)*BesselK(1/3, -(2/9)*3^(1/2)*(x+zeta)*(-(x+zeta)/(-t)^(1/3))^(1/2)/(-t)^(1/3))/(-t)^(1/3), zeta = -infinity .. infinity))/Pi^2

(25)

 

Example 13: A PDE & BC problem that is solved using Laplace transform:

> 

pde__13 := diff(u(x, y), y, x) = sin(x)*sin(y)

> 

iv__13 := u(x, 0) = 1+cos(x), (D[2](u))(0, y) = -2*sin(y)

> 

pdsolve([pde__13, iv__13])

u(x, y) = (1/2)*cos(x-y)+(1/2)*cos(x+y)+cos(y)

(26)

To see the computational flow, the solving methods used and in which order they are tried use

> 

infolevel[pdsolve] := 2

2

(27)

Example 14:

> 

pde__14 := diff(u(x, y), x, x)+diff(u(x, y), y, y) = 0

> 

iv__14 := u(x, 0) = 0, u(x, b) = h(x)

> 

pdsolve([pde__14, iv__14])

* trying method "SpecializeArbitraryFunctions" for 2nd order PDEs
   -> trying "LinearInXT"
   -> trying "HomogeneousBC"
* trying method "SpecializeArbitraryConstants" for 2nd order PDEs
* trying method "Wave" for 2nd order PDEs
   -> trying "Cauchy"
   -> trying "SemiInfiniteDomain"
   -> trying "WithSourceTerm"
* trying method "Heat" for 2nd order PDEs
   -> trying "SemiInfiniteDomain"
   -> trying "WithSourceTerm"
* trying method "Series" for 2nd order PDEs
   -> trying "ThreeBCsincos"
   -> trying "FourBC"
   -> trying "ThreeBC"
   -> trying "ThreeBCPeriodic"
   -> trying "WithSourceTerm"
      * trying method "SpecializeArbitraryFunctions" for 2nd order PDEs
         -> trying "LinearInXT"
         -> trying "HomogeneousBC"
            Trying travelling wave solutions as power series in tanh ...
               Trying travelling wave solutions as power series in ln ...
      * trying method "SpecializeArbitraryConstants" for 2nd order PDEs
         Trying travelling wave solutions as power series in tanh ...
            Trying travelling wave solutions as power series in ln ...
      * trying method "Wave" for 2nd order PDEs
         -> trying "Cauchy"
         -> trying "SemiInfiniteDomain"
         -> trying "WithSourceTerm"
      * trying method "Heat" for 2nd order PDEs
         -> trying "SemiInfiniteDomain"
         -> trying "WithSourceTerm"
      * trying method "Series" for 2nd order PDEs
         -> trying "ThreeBCsincos"
         -> trying "FourBC"
         -> trying "ThreeBC"
         -> trying "ThreeBCPeriodic"
         -> trying "WithSourceTerm"
         -> trying "ThreeVariables"
      * trying method "Laplace" for 2nd order PDEs
         -> trying a Laplace transformation
      * trying method "Fourier" for 2nd order PDEs
         -> trying a fourier transformation

      * trying method "Generic" for 2nd order PDEs
         -> trying a solution in terms of arbitrary constants and functions to be adjusted to the given initial conditions
      * trying method "PolynomialSolutions" for 2nd order PDEs

      * trying method "LinearDifferentialOperator" for 2nd order PDEs
      * trying method "Superposition" for 2nd order PDEs
   -> trying "ThreeVariables"
* trying method "Laplace" for 2nd order PDEs
   -> trying a Laplace transformation
* trying method "Fourier" for 2nd order PDEs
   -> trying a fourier transformation

   <- fourier transformation successful
<- method "Fourier" for 2nd order PDEs successful

 

u(x, y) = invfourier(exp(s*(b+y))*fourier(h(x), x, s)/(exp(2*s*b)-1), s, x)-invfourier(exp(s*(b-y))*fourier(h(x), x, s)/(exp(2*s*b)-1), s, x)

(28)
> 

convert(u(x, y) = invfourier(exp(s*(b+y))*fourier(h(x), x, s)/(exp(2*s*b)-1), s, x)-invfourier(exp(s*(b-y))*fourier(h(x), x, s)/(exp(2*s*b)-1), s, x), Int)

u(x, y) = (1/2)*(Int((Int(h(x)*exp(-I*x*s), x = -infinity .. infinity))*exp(s*(b+y)+I*s*x)/(exp(2*s*b)-1), s = -infinity .. infinity))/Pi-(1/2)*(Int((Int(h(x)*exp(-I*x*s), x = -infinity .. infinity))*exp(s*(b-y)+I*s*x)/(exp(2*s*b)-1), s = -infinity .. infinity))/Pi

(29)

Reset the infolevel to avoid displaying the computational flow:

> 

infolevel[pdsolve] := 1

7. Improvements to solving heat and wave PDE, with or without a source:

 

Example 15: A heat PDE:

> 

pde__15 := diff(u(x, t), t) = 13*(diff(u(x, t), x, x))

> 

iv__15 := (D[1](u))(0, t) = 0, (D[1](u))(1, t) = 1, u(x, 0) = (1/2)*x^2+x

> 

pdsolve([pde__15, iv__15], u(x, t))

u(x, t) = 1/2+Sum(2*cos(n*Pi*x)*exp(-13*Pi^2*n^2*t)*(-1+(-1)^n)/(Pi^2*n^2), n = 1 .. infinity)+13*t+(1/2)*x^2

(30)

To verify an infinite series solution such as this one you can first use pdetest

> 

pdetest(u(x, t) = 1/2+Sum(2*cos(n*Pi*x)*exp(-13*Pi^2*n^2*t)*(-1+(-1)^n)/(Pi^2*n^2), n = 1 .. infinity)+13*t+(1/2)*x^2, [pde__15, iv__15])

[0, 0, 0, 1/2+Sum(2*cos(n*Pi*x)*(-1+(-1)^n)/(Pi^2*n^2), n = 1 .. infinity)-x]

(31)

To verify that the last condition, for u(x, 0) is satisfied, we plot the first 1000 terms of the series solution with t = 0 and make sure that it coincides with the plot of  the right-hand side of the initial condition u(x, 0) = (1/2)*x^2+x. Expected: the two plots superimpose each other

> 

plot([value(subs(t = 0, infinity = 1000, rhs(u(x, t) = 1/2+Sum(2*cos(n*Pi*x)*exp(-13*Pi^2*n^2*t)*(-1+(-1)^n)/(Pi^2*n^2), n = 1 .. infinity)+13*t+(1/2)*x^2))), (1/2)*x^2+x], x = 0 .. 1)

 

Example 16: A heat PDE in a semi-bounded domain:

> 

pde__16 := diff(u(x, t), t) = (1/4)*(diff(u(x, t), x, x))

> 

iv__16 := (D[1](u))(alpha, t) = 0, u(x, beta) = 10*exp(-x^2)

> 

`assuming`([pdsolve([pde__16, iv__16], u(x, t))], [0 < x, 0 < t])

u(x, t) = -5*exp(x^2/(-t+beta-1))*((erf(((t-beta-1)*alpha+x)/((t-beta+1)^(1/2)*(t-beta)^(1/2)))-1)*exp(4*alpha*(-x+alpha)/(-t+beta-1))+erf(((t-beta+1)*alpha-x)/((t-beta+1)^(1/2)*(t-beta)^(1/2)))-1)/(t-beta+1)^(1/2)

(32)

 

Example 17: A wave PDE in a semi-bounded domain:

> 

pde__17 := diff(u(x, t), t, t)-9*(diff(u(x, t), x, x)) = 0

> 

iv__17 := (D[1](u))(0, t) = 0, u(x, 0) = 0, (D[2](u))(x, 0) = x^3

> 

`assuming`([pdsolve([pde__17, iv__17])], [0 < x, 0 < t])

u(x, t) = piecewise(3*t < x, 9*t^3*x+t*x^3, x < 3*t, (27/4)*t^4+(9/2)*t^2*x^2+(1/12)*x^4)

(33)

 

Example 18: A wave PDE with a source

> 

pde__18 := diff(u(x, t), t, t) = diff(u(x, t), x, x)+x*exp(-t)

> 

iv__18 := u(0, t) = 0, u(1, t) = 0, u(x, 0) = 0, (D[2](u))(x, 0) = 1

> 

pdsolve([pde__18, iv__18])

u(x, t) = Sum(((-Pi^2*(-1)^n*n^2+Pi^2*n^2+2*(-1)^(n+1)+1)*cos(n*Pi*(t-x))-Pi*(-1)^n*n*sin(n*Pi*(t-x))+(Pi^2*(-1)^n*n^2-Pi^2*n^2+2*(-1)^n-1)*cos(n*Pi*(t+x))+Pi*n*(2*exp(-t)*(-1)^(n+1)*sin(n*Pi*x)+sin(n*Pi*(t+x))*(-1)^n))/(Pi^2*n^2*(Pi^2*n^2+1)), n = 1 .. infinity)

(34)

 

Example 19: Another wave PDE with a source

> 

pde__19 := diff(u(x, t), t, t) = 4*(diff(u(x, t), x, x))+(1+t)*x

> 

iv__19 := u(0, t) = 0, u(Pi, t) = sin(t), u(x, 0) = 0, (D[2](u))(x, 0) = 0

> 

pdsolve([pde__19, iv__19])

u(x, t) = ((Sum(-2*((1/2)*cos(n*x-t)*n^3-(1/2)*cos(n*x+t)*n^3+((-2*n^4-(1/2)*Pi*n^2+(1/8)*Pi)*sin(2*n*t)+(t-cos(2*n*t)+1)*n*(n-1/2)*Pi*(n+1/2))*sin(n*x))*(-1)^n/(Pi*n^4*(4*n^2-1)), n = 1 .. infinity))*Pi+x*sin(t))/Pi

(35)

8. Improvements in series methods for Laplace PDE problems

 

"  Example 20:A Laplace PDE with BC representing the inside of a quarter circle of radius 1. The solution we seek is bounded as r approaches 0:"

> 

pde__20 := diff(u(r, theta), r, r)+(diff(u(r, theta), r))/r+(diff(u(r, theta), theta, theta))/r^2 = 0

> 

iv__20 := u(r, 0) = 0, u(r, (1/2)*Pi) = 0, (D[1](u))(1, theta) = f(theta)

> 

`assuming`([pdsolve([pde__20, iv__20], u(r, theta), HINT = boundedseries(r = [0]))], [0 <= theta, theta <= (1/2)*Pi, 0 <= r, r <= 1])

u(r, theta) = Sum(2*(Int(f(theta)*sin(2*n*theta), theta = 0 .. (1/2)*Pi))*r^(2*n)*sin(2*n*theta)/(Pi*n), n = 1 .. infinity)

(36)

 

Example 21: A Laplace PDE for which we seek a solution that remains bounded as y approaches infinity:

> 

pde__21 := diff(u(x, y), x, x)+diff(u(x, y), y, y) = 0

> 

iv__21 := u(0, y) = A, u(a, y) = 0, u(x, 0) = 0

> 

`assuming`([pdsolve([pde__21, iv__21], HINT = boundedseries(y = infinity))], [a > 0])

u(x, y) = ((Sum(-2*A*sin(n*Pi*x/a)*exp(-n*Pi*y/a)/(n*Pi), n = 1 .. infinity))*a-A*(x-a))/a

(37)

 

9. Better simplification of answers:

 

 

Example 22: For this wave PDE with a source term, pdsolve used to return a solution with uncomputed integrals:

> 

pde__22 := diff(u(x, t), t, t) = A*x+diff(u(x, t), x, x)

> 

iv__22 := u(0, t) = 0, u(1, t) = 0, u(x, 0) = 0, (D[2](u))(x, 0) = 0

> 

pdsolve([pde__22, iv__22], u(x, t))

u(x, t) = Sum(2*(-1)^n*A*sin(n*Pi*x)*cos(n*Pi*t)/(n^3*Pi^3), n = 1 .. infinity)+(1/6)*(-x^3+x)*A

(38)

 

Example 23: A BC at x = infinityis now handled by pdsolve:

> 

pde__23 := diff(u(x, y), x, x)+diff(u(x, y), y, y) = 0

> 

iv__23 := u(0, y) = sin(y), u(x, 0) = 0, u(x, a) = 0, u(infinity, y) = 0

> 

`assuming`([pdsolve([pde__23, iv__23], u(x, y))], [0 < a])

u(x, y) = Sum(2*piecewise(a = Pi*n, (1/2)*Pi*n, -Pi*(-1)^n*sin(a)*n*a/(Pi^2*n^2-a^2))*exp(-n*Pi*x/a)*sin(n*Pi*y/a)/a, n = 1 .. infinity)

(39)

 

Example 24: A reduced Helmholtz PDE in a square of side "Pi. "Previously, pdsolve returned a series starting at n = 0, when the limit of the n = 0 term is 0.

> 

pde__24 := diff(u(x, y), x, x)+diff(u(x, y), y, y)-k*u(x, y) = 0

> 

iv__24 := u(0, y) = 1, u(Pi, y) = 0, u(x, 0) = 0, u(x, Pi) = 0

> 

`assuming`([pdsolve([pde__24, iv__24], u(x, y))], [0 < k])

u(x, y) = Sum(-2*sin(n*y)*(-1+(-1)^n)*(exp(-(-2*Pi+x)*(n^2+k)^(1/2))-exp((n^2+k)^(1/2)*x))/((exp(2*(n^2+k)^(1/2)*Pi)-1)*Pi*n), n = 1 .. infinity)

(40)

 

10. Linear differential operator: more solutions are now successfully computed

 

 

Example 25:

> 

pde__25 := diff(w(x1, x2, x3, t), t) = diff(w(x1, x2, x3, t), x1, x1)+diff(w(x1, x2, x3, t), x2, x2)+diff(w(x1, x2, x3, t), x3, x3)

> 

iv__25 := w(x1, x2, x3, 1) = exp(a)*x1^2+x2*x3

> 

pdsolve([pde__25, iv__25])

w(x1, x2, x3, t) = (x1^2+2*t-2)*exp(a)+x2*x3

(41)

 

Example 26:

> 

pde__26 := diff(w(x1, x2, x3, t), t)-(D[1, 2](w))(x1, x2, x3, t)-(D[1, 3](w))(x1, x2, x3, t)-(D[3, 3](w))(x1, x2, x3, t)+(D[2, 3](w))(x1, x2, x3, t) = 0

> 

iv__26 := w(x1, x2, x3, a) = exp(x1)+x2-3*x3

> 

pdsolve([pde__26, iv__26])

w(x1, x2, x3, t) = exp(x1)+x2-3*x3

(42)

 

Example 27:

> 

pde__27 := diff(w(x1, x2, x3, t), t, t) = (D[1, 2](w))(x1, x2, x3, t)+(D[1, 3](w))(x1, x2, x3, t)+(D[3, 3](w))(x1, x2, x3, t)-(D[2, 3](w))(x1, x2, x3, t)

> 

iv__27 := w(x1, x2, x3, a) = x1^3*x2^2+x3, (D[4](w))(x1, x2, x3, a) = -x2*x3+x1

> 

pdsolve([pde__27, iv__27], w(x1, x2, x3, t))

w(x1, x2, x3, t) = x1^3*x2^2+3*x2*(-t+a)^2*x1^2+(1/2)*(-t+a)*(a^3-3*a^2*t+3*a*t^2-t^3-2)*x1-(1/6)*a^3+(1/2)*a^2*t+(1/6)*(-3*t^2+6*x2*x3)*a+(1/6)*t^3-t*x2*x3+x3

(43)

 

 

11. More problems in 3 variables are now solved

 

 

Example 28: A Schrödinger type PDE in two space dimensions, where Z is Planck's constant.

> 

pde__28 := I*`&hbar;`*(diff(f(x, y, t), t)) = -`&hbar;`^2*(diff(f(x, y, t), x, x)+diff(f(x, y, t), y, y))/(2*m)

> 

iv__28 := f(x, y, 0) = sqrt(2)*(sin(2*Pi*x)*sin(Pi*y)+sin(Pi*x)*sin(3*Pi*y)), f(0, y, t) = 0, f(1, y, t) = 0, f(x, 1, t) = 0, f(x, 0, t) = 0

> 

pdsolve([pde__28, iv__28], f(x, y, t))

f(x, y, t) = 2^(1/2)*sin(Pi*x)*(2*exp(-((5/2)*I)*`&hbar;`*t*Pi^2/m)*cos(Pi*x)*sin(Pi*y)+exp(-(5*I)*`&hbar;`*t*Pi^2/m)*sin(3*Pi*y))

(44)

 

Example 29: This problem represents the temperature distribution in a thin rectangular plate whose lateral surfaces are insulated yet is losing heat by convection along the boundary x = 1, into a surrounding medium at temperature 0 (Articolo example 6.6.3):

> 

pde__29 := diff(u(x, y, t), t) = 1/50*(diff(u(x, y, t), x, x)+diff(u(x, y, t), y, y))

> 

iv__29 := (D[1](u))(0, y, t) = 0, (D[1](u))(1, y, t)+u(1, y, t) = 0, u(x, 0, t) = 0, u(x, 1, t) = 0, u(x, y, 0) = (1-(1/3)*x^2)*y*(1-y)

> 

`assuming`([pdsolve([pde__29, iv__29], u(x, y, t))], [0 <= x, x <= 1, 0 <= y, y <= 1])

u(x, y, t) = `casesplit/ans`(Sum(Sum((32/3)*exp(-(1/50)*t*(Pi^2*n^2+lambda[n1]^2))*(-1+(-1)^n)*cos(lambda[n1]*x)*sin(n*Pi*y)*(-lambda[n1]^2*sin(lambda[n1])+lambda[n1]*cos(lambda[n1])-sin(lambda[n1]))/(Pi^3*n^3*lambda[n1]^2*(sin(2*lambda[n1])+2*lambda[n1])), n1 = 0 .. infinity), n = 1 .. infinity), {And(tan(lambda[n1])*lambda[n1]-1 = 0, 0 < lambda[n1])})

(45)

 

Articolo's Exercise 7.15, with 6 boundary/initial conditions, two for each variable

> 

pde__30 := diff(u(x, y, t), t, t) = 1/4*(diff(u(x, y, t), x, x)+diff(u(x, y, t), y, y))-(1/10)*(diff(u(x, y, t), t))

> 

iv__30 := (D[1](u))(0, y, t) = 0, (D[1](u))(1, y, t)+u(1, y, t) = 0, (D[2](u))(x, 0, t)-u(x, 0, t) = 0, (D[2](u))(x, 1, t) = 0, u(x, y, 0) = (1-(1/3)*x^2)*(1-(1/3)*(y-1)^2), (D[3](u))(x, y, 0) = 0

 

This problem is tricky ... There are three independent variables, therefore two eigenvalues (constants that appear separating variables by product) in the Sturm-Liouville problem. But after solving the separated system and also for the eigenvalues, the second eigenvalue is equal to the first one, and in addition cannot be expressed in terms of known functions, because the equation it solves cannot be inverted.

 

> 

pdsolve([pde__30, iv__30])

u(x, y, t) = `casesplit/ans`(Sum((1/6)*(lambda[n]^2*sin(lambda[n])-lambda[n]*cos(lambda[n])+sin(lambda[n]))*cos(lambda[n]*x)*(exp((1/10)*t*(-200*lambda[n]^2+1)^(1/2))*(-200*lambda[n]^2+1)^(1/2)+exp((1/10)*t*(-200*lambda[n]^2+1)^(1/2))+(-200*lambda[n]^2+1)^(1/2)-1)*exp(-(1/20)*t*((-200*lambda[n]^2+1)^(1/2)+1))*(cos(lambda[n]*y)*lambda[n]+sin(lambda[n]*y))*(6*lambda[n]^2*cos(lambda[n])^2+cos(lambda[n])^2-5*lambda[n]^2-1)/((-200*lambda[n]^2+1)^(1/2)*(-cos(lambda[n])^4+(lambda[n]*sin(lambda[n])-1)*cos(lambda[n])^3+(lambda[n]^2+lambda[n]*sin(lambda[n])+1)*cos(lambda[n])^2+(lambda[n]^2+2*lambda[n]*sin(lambda[n])+1)*cos(lambda[n])+lambda[n]*(lambda[n]+sin(lambda[n])))*lambda[n]^4*(cos(lambda[n])-1)), n = 0 .. infinity), {And(tan(lambda[n])*lambda[n]-1 = 0, 0 < lambda[n])})

(46)
> 

``

 


 

Download PDE_and_BC_during_2018.mw

Edgardo S. Cheb-Terrab
Physics, Differential Equations and Mathematical Functions, Maplesoft


 

Solving PDEs with initial and boundary conditions:

Sturm-Liouville problem with RootOf eigenvalues

 

Computer algebra systems always failed to compute exact solutions for a linear PDE with initial / boundary conditions when the eigenvalues of the corresponding Sturm-Liouville problem cannot be solved exactly - that is, when they can only be represented at most abstractly, using a RootOf construction.

 

This post illustrates then a new Maple development, to tackle this kind of problem (work in collaboration with Katherina von Bülow), including testing and plotting the resulting exact solution. To reproduce the computation below in Maple 2018.1 you need to install the Maplesoft Physics Updates (version 134 or higher).

 

As an example, consider

> 

pde := diff(u(x, t), t) = k*(diff(u(x, t), x, x)); iv := u(x, 0) = 1-(1/4)*x^3, eval(diff(u(x, t), x), x = 0) = 0, eval(diff(u(x, t), x), x = 1)+u(1, t) = 0

diff(u(x, t), t) = k*(diff(diff(u(x, t), x), x))

 

u(x, 0) = 1-(1/4)*x^3, eval(diff(u(x, t), x), {x = 0}) = 0, eval(diff(u(x, t), x), {x = 1})+u(1, t) = 0

(1)

This problem represents the temperature distribution in a thin rod whose lateral surface is insulated over the interval 0 < x and x < 1, with the left end of the rod insulated and the right end experiencing a convection heat loss, as explained in George A. Articolo's Partial Differential Equations and Boundary Value Problems with Maple, example 3.6.4.

 

The formulation as a Sturm-Liouville problem starts with solving the PDE by separating the variables by product

> 

pdsolve(pde, HINT = `*`)

PDESolStruc(u(x, t) = _F1(x)*_F2(t), [{diff(_F2(t), t) = k*_c[1]*_F2(t), diff(diff(_F1(x), x), x) = _c[1]*_F1(x)}])

(2)

Substituting this separation by product into the last two (out of three) initial/boundary conditions (iv), the original pde and these conditions are transformed into an ODE system with initial conditions

> 

{_F1(1)+(D(_F1))(1) = 0, diff(_F2(t), t) = -k*_c[1]*_F2(t), diff(_F1(x), x, x) = -_c[1]*_F1(x), (D(_F1))(0) = 0}

{_F1(1)+(D(_F1))(1) = 0, diff(_F2(t), t) = -k*_c[1]*_F2(t), diff(diff(_F1(x), x), x) = -_c[1]*_F1(x), (D(_F1))(0) = 0}

(3)

This is a problem in actually three variables, including _c[1], and the solution can be computed using dsolve

> 

dsolve({_F1(1)+(D(_F1))(1) = 0, diff(_F2(t), t) = -k*_c[1]*_F2(t), diff(diff(_F1(x), x), x) = -_c[1]*_F1(x), (D(_F1))(0) = 0}, {_F1, _F2, _c[1]})

{_c[1] = 0, _F1(x) = 0, _F2(t) = _C5}, {_c[1] = _C2, _F1(x) = 0, _F2(t) = _C5/exp(k*_C2*t)}, {_c[1] = RootOf(tan(_Z)*(_Z^2)^(1/2)-1)^2, _F1(x) = _C4*cos((RootOf(tan(_Z)*(_Z^2)^(1/2)-1)^2)^(1/2)*x), _F2(t) = _C5/exp(k*RootOf(tan(_Z)*(_Z^2)^(1/2)-1)^2*t)}

(4)

We are interested in a solution of the form u(x, t) = _F1(x)*_F2(t) and _F1(x)*_F2(t) <> 0, so discard the first two in the above and keep only the third one

> 

solution_to_SL := ({_c[1] = 0, _F1(x) = 0, _F2(t) = _C5}, {_c[1] = _C2, _F1(x) = 0, _F2(t) = _C5/exp(k*_C2*t)}, {_c[1] = RootOf(tan(_Z)*(_Z^2)^(1/2)-1)^2, _F1(x) = _C4*cos((RootOf(tan(_Z)*(_Z^2)^(1/2)-1)^2)^(1/2)*x), _F2(t) = _C5/exp(k*RootOf(tan(_Z)*(_Z^2)^(1/2)-1)^2*t)})[3]

{_c[1] = RootOf(tan(_Z)*(_Z^2)^(1/2)-1)^2, _F1(x) = _C4*cos((RootOf(tan(_Z)*(_Z^2)^(1/2)-1)^2)^(1/2)*x), _F2(t) = _C5/exp(k*RootOf(tan(_Z)*(_Z^2)^(1/2)-1)^2*t)}

(5)

If we were able to express _c[1] in closed form, with a formula depending on an integer parameter identifying one of the roots of the expression, the rest of the method is straightforward, the product u(x, t) = _F1(x)*_F2(t) is a solution that by construction satisfies the boundary conditions in (1) , and we have one of them for each value of _c[1](for each root of the expression within the RootOf construction). The first condition in iv is used to adjust the remaining constant (combine _C4 times _C5 into one) using orthogonality properties , and by linearly superimposing these different solutions we construct an infinite series solution. All this is explained in standard textbooks.

 

The problem is what to do when _c[1] cannot be expressed in closed form, as in the example above. To understand the situation clearly, remove that RootOf and plot its contents:

> 

RootOf(tan(_Z)*sqrt(_Z^2)-1) = lambda[n]

RootOf(tan(_Z)*(_Z^2)^(1/2)-1) = lambda[n]

(6)
> 

DEtools[remove_RootOf](RootOf(tan(_Z)*(_Z^2)^(1/2)-1) = lambda[n])

tan(lambda[n])*(lambda[n]^2)^(1/2)-1 = 0

(7)

This equation has infinitely many roots. For instance between -2*Pi and 2*Pi, NULL

> 

plot(lhs(tan(lambda[n])*(lambda[n]^2)^(1/2)-1 = 0), lambda[n] = -2*Pi .. 2*Pi)

 

A closed form solution to represent these values of `&lambda;__n` (also called the eigenvalue of the Sturm-Liouville problem) are necessary in the intermediate solving steps, and to express in closed form the solution to the original problem.

 

We cannot do magic to overcome this mathematical limitation, but there are in Maple representational abstract alternatives: we can use, in all the intermediate computations, a RootOf construction with a label  identifying each of the roots and, at the end, remove the RootOf construction, presenting something readable, understandable.

 

This is the representation of the solution that we are proposing, whose automatic derivation from given pde and iv is already implemented:

> 

pdsolve([pde, iv])

u(x, t) = `casesplit/ans`(Sum(3*exp(-k*lambda[n]^2*t)*cos(lambda[n]*x)*((-lambda[n]^2+2)*cos(lambda[n])-2+(lambda[n]^3+2*lambda[n])*sin(lambda[n]))/(lambda[n]^3*(sin(2*lambda[n])+2*lambda[n])), n = 0 .. infinity), {And(tan(lambda[n])*lambda[n]-1 = 0, 0 <= lambda[n])})

(8)

So an expression restricted by equations, inequations and possibly subject to conditions. This is the same type of representation we use in pdsolve, PDEtools:-casesplit and the FunctionAdvisor.

 

Note that, since there are infinitely many roots to the left and to the right of the origin, we assumed for simplicity that `&lambda;__n` >= 0, which suffices to construct the infinite series solution above. The initial value for the summation index n could be 0 or 1, it doesn't matter, since n itself does not appear in the summand, it only identifies one of the values of lambda solving tan(lambda[n])*lambda[n]-1 = 0. This is a very nice development.

 

So we can now compute and represent the solution algebraically even when the eigenvalue `&lambda;__n` cannot be expressed in closed form. But how do you test such a solution? Or even plot it?

 

For now that needs some human intervention. Start with testing (part of what follows is in the plans for further development of pdetest). Separate the solution expressed in terms of `&lambda;__n`from the equation defining it

> 

solution := lhs(u(x, t) = `casesplit/ans`(Sum(3*exp(-k*lambda[n]^2*t)*cos(lambda[n]*x)*((-lambda[n]^2+2)*cos(lambda[n])-2+(lambda[n]^3+2*lambda[n])*sin(lambda[n]))/(lambda[n]^3*(sin(2*lambda[n])+2*lambda[n])), n = 0 .. infinity), {And(tan(lambda[n])*lambda[n]-1 = 0, 0 <= lambda[n])})) = op(1, rhs(u(x, t) = `casesplit/ans`(Sum(3*exp(-k*lambda[n]^2*t)*cos(lambda[n]*x)*((-lambda[n]^2+2)*cos(lambda[n])-2+(lambda[n]^3+2*lambda[n])*sin(lambda[n]))/(lambda[n]^3*(sin(2*lambda[n])+2*lambda[n])), n = 0 .. infinity), {And(tan(lambda[n])*lambda[n]-1 = 0, 0 <= lambda[n])})))

u(x, t) = Sum(3*exp(-k*lambda[n]^2*t)*cos(lambda[n]*x)*((-lambda[n]^2+2)*cos(lambda[n])-2+(lambda[n]^3+2*lambda[n])*sin(lambda[n]))/(lambda[n]^3*(sin(2*lambda[n])+2*lambda[n])), n = 0 .. infinity)

(9)
> 

op([2, 2, 1, 1], u(x, t) = `casesplit/ans`(Sum(3*exp(-k*lambda[n]^2*t)*cos(lambda[n]*x)*((-lambda[n]^2+2)*cos(lambda[n])-2+(lambda[n]^3+2*lambda[n])*sin(lambda[n]))/(lambda[n]^3*(sin(2*lambda[n])+2*lambda[n])), n = 0 .. infinity), {And(tan(lambda[n])*lambda[n]-1 = 0, 0 <= lambda[n])}))

tan(lambda[n])*lambda[n]-1 = 0

(10)

By inspection, solution has sin(lambda[n]) and cos(lambda[n]), not tan(lambda[n]), so rewrite (10), and on the way isolate cos(lambda[n])

> 

condition := isolate(convert(tan(lambda[n])*lambda[n]-1 = 0, sincos), cos(lambda[n]))

cos(lambda[n]) = sin(lambda[n])*lambda[n]

(11)

Start by pdetesting

> 

pdetest(solution, [pde, iv])

[0, Sum(3*cos(lambda[n]*x)*((I*lambda[n]^3+(2*I)*lambda[n]-lambda[n]^2+2)*exp(-I*lambda[n])-4+(-I*lambda[n]^3-(2*I)*lambda[n]-lambda[n]^2+2)*exp(I*lambda[n]))/(2*lambda[n]^3*sin(2*lambda[n])+4*lambda[n]^4), n = 0 .. infinity)-1+(1/4)*x^3, 0, Sum(-3*((I*lambda[n]^3+(2*I)*lambda[n]-lambda[n]^2+2)*exp(-I*lambda[n])-4+(-I*lambda[n]^3-(2*I)*lambda[n]-lambda[n]^2+2)*exp(I*lambda[n]))*sin(lambda[n])*exp(-k*lambda[n]^2*t)/(2*lambda[n]^2*sin(2*lambda[n])+4*lambda[n]^3), n = 0 .. infinity)+Sum(3*((I*lambda[n]^3+(2*I)*lambda[n]-lambda[n]^2+2)*exp(-I*lambda[n])-4+(-I*lambda[n]^3-(2*I)*lambda[n]-lambda[n]^2+2)*exp(I*lambda[n]))*cos(lambda[n])*exp(-k*lambda[n]^2*t)/(2*lambda[n]^3*sin(2*lambda[n])+4*lambda[n]^4), n = 0 .. infinity)]

(12)

A further manipulation, substituting condition and combining the sums results in

> 

simplify(subs(condition, combine([0, Sum(3*cos(lambda[n]*x)*((I*lambda[n]^3+(2*I)*lambda[n]-lambda[n]^2+2)*exp(-I*lambda[n])-4+(-I*lambda[n]^3-(2*I)*lambda[n]-lambda[n]^2+2)*exp(I*lambda[n]))/(2*lambda[n]^3*sin(2*lambda[n])+4*lambda[n]^4), n = 0 .. infinity)-1+(1/4)*x^3, 0, Sum(-3*((I*lambda[n]^3+(2*I)*lambda[n]-lambda[n]^2+2)*exp(-I*lambda[n])-4+(-I*lambda[n]^3-(2*I)*lambda[n]-lambda[n]^2+2)*exp(I*lambda[n]))*sin(lambda[n])*exp(-k*lambda[n]^2*t)/(2*lambda[n]^2*sin(2*lambda[n])+4*lambda[n]^3), n = 0 .. infinity)+Sum(3*((I*lambda[n]^3+(2*I)*lambda[n]-lambda[n]^2+2)*exp(-I*lambda[n])-4+(-I*lambda[n]^3-(2*I)*lambda[n]-lambda[n]^2+2)*exp(I*lambda[n]))*cos(lambda[n])*exp(-k*lambda[n]^2*t)/(2*lambda[n]^3*sin(2*lambda[n])+4*lambda[n]^4), n = 0 .. infinity)], Sum)))

[0, Sum(3*cos(lambda[n]*x)*((I*lambda[n]^3+(2*I)*lambda[n]-lambda[n]^2+2)*exp(-I*lambda[n])-4+(-I*lambda[n]^3-(2*I)*lambda[n]-lambda[n]^2+2)*exp(I*lambda[n]))/(2*lambda[n]^3*sin(2*lambda[n])+4*lambda[n]^4), n = 0 .. infinity)-1+(1/4)*x^3, 0, 0]

(13)

So from the four elements (one PDE and three iv), three are satisfied without having to specify who is `&lambda;__n` - it sufficed to say that cos(lambda[n]) = sin(lambda[n])*lambda[n], and this is the case of most of the examples we analyzed. You really don't need the exact closed form of `&lambda;__n` in these examples.

For the one expression which remains to be proven equal to zero, there is no clear way to perform the sum and show that it is equal to 1-(1/4)*x^3 without further information on the value of `&lambda;__n`.  So this part needs to be tested using a plot.

 

We need to perform the summation, instead of using infinite terms, using, say, the first 100 of them, and for that purpose we need the first 100 positive values of `&lambda;__n`. These values can be obtained using fsolve. Increase Digits to get a better approximation:

> 

Digits := 20

20

(14)
> 

L := [fsolve(condition, lambda[n], 0 .. 10^10, maxsols = 100)]

[.86033358901937976248, 3.4256184594817281465, 6.4372981791719471204, 9.5293344053619636029, 12.645287223856643104, 15.771284874815882047, 18.902409956860024151, 22.036496727938565083, 25.172446326646664714, 28.309642854452012364, 31.447714637546233553, 34.586424215288923664, 37.725612827776501051, 40.865170330488067836, 44.005017920830842726, 47.145097736761031075, 50.285366337773650463, 53.425790477394666341, 56.566344279821518125, 59.707007305335457045, 62.847763194454453706, 65.988598698490388394, 69.129502973895260447, 72.270467060308959618, 75.411483488848152399, 78.552545984242931733, 81.693649235601687434, 84.834788718042289254, 87.975960552493213068, 91.117161394464748699, 94.258388345039861151, 97.399638879073768561, 100.54091078684231954, 103.68220212628958019, 106.82351118369472752, 109.96483644107604716, 113.10617654902325890, 116.24753030393208866, 119.38889662883081820, 122.53027455715460386, 125.67166321895208822, 128.81306182910932798, 131.95446967725504430, 135.09588611907366660, 138.23731056880233903, 141.37874249272782439, 144.52018140353123171, 147.66162685535436266, 150.80307843948249426, 153.94453578055557821, 157.08599853323391302, 160.22746637925593824, 163.36893902483538786, 166.51041619835300015, 169.65189764830461611, 172.79338314147304750, 175.93487246129575380, 179.07636540640428965, 182.21786178931479917, 185.35936143525164220, 188.50086418108862526, 191.64236987439434602, 194.78387837256989967, 197.92538954206868814, 201.06690325768935430, 204.20841940193396857, 207.34993786442454883, 210.49145854137182078, 213.63298133509084236, 216.77450615355873900, 219.91603291001033966, 223.05756152256797778, 226.19909191390213620, 229.34062401091997921, 232.48215774447913415, 235.62369304912436649, 238.76522986284504017, 241.90676812685147396, 245.04830778536849850, 248.18984878544468993, 251.33139107677590895, 254.47293461154190896, 257.61447934425489811, 260.75602523161904694, 263.89757223240002997, 267.03912030730377471, 270.18066941886366904, 273.32221953133554631, 276.46377061059982966, 279.60532262407027259, 282.74687554060878265, 285.88842933044586060, 289.02998396510622761, 292.17153941733925030, 295.31309566105380609, 298.45465267125726198, 301.59621042399826673, 304.73776889631308125, 307.87932806617519465, 311.02088791244799345]

(15)

For convenience, construct a procedure, as a function of n, that returns each of these values

> 

Lambda := proc (n) options operator, arrow; if n::nonnegint then L[n+1] else 'procname(args)' end if end proc

proc (n) options operator, arrow; if n::nonnegint then L[n+1] else 'procname(args)' end if end proc

(16)

Replace `&lambda;__n` by "Lambda(n), "infinity by 99 and the expression to be plotted is

> 

remain := subs(lambda[n] = Lambda(n), infinity = 99, [0, Sum(3*cos(lambda[n]*x)*((I*lambda[n]^3+(2*I)*lambda[n]-lambda[n]^2+2)*exp(-I*lambda[n])-4+(-I*lambda[n]^3-(2*I)*lambda[n]-lambda[n]^2+2)*exp(I*lambda[n]))/(2*lambda[n]^3*sin(2*lambda[n])+4*lambda[n]^4), n = 0 .. infinity)-1+(1/4)*x^3, 0, 0][2])

Sum(3*cos(Lambda(n)*x)*((I*Lambda(n)^3+(2*I)*Lambda(n)-Lambda(n)^2+2)*exp(-I*Lambda(n))-4+(-I*Lambda(n)^3-(2*I)*Lambda(n)-Lambda(n)^2+2)*exp(I*Lambda(n)))/(2*Lambda(n)^3*sin(2*Lambda(n))+4*Lambda(n)^4), n = 0 .. 99)-1+(1/4)*x^3

(17)

Perform the sum and plot. The plotting range is the one present in the iv (1), x goes from 0 to 1

> 

R := eval(remain, Sum = add)

> 

plot(R, x = 0 .. 1)

 

This result is very good. With the first 100 terms (constructed using the first 100 roots of tan(lambda[n])*lambda[n]-1 = 0) we verified that this last of the four testing conditions is sufficiently close to zero, and this concludes the verification.

To plot the solution, the idea is the same one: in (9), give a value to k - say k = 1/5 - then construct the sum of the first 100 terms as done in (17), but this time using (9) instead of (13)

 

> 

subs(k = 1/5, lambda[n] = Lambda(n), infinity = 99, u(x, t) = Sum(3*exp(-k*lambda[n]^2*t)*cos(lambda[n]*x)*((-lambda[n]^2+2)*cos(lambda[n])-2+(lambda[n]^3+2*lambda[n])*sin(lambda[n]))/(lambda[n]^3*(sin(2*lambda[n])+2*lambda[n])), n = 0 .. infinity))

u(x, t) = Sum(3*exp(-(1/5)*Lambda(n)^2*t)*cos(Lambda(n)*x)*((-Lambda(n)^2+2)*cos(Lambda(n))-2+(Lambda(n)^3+2*Lambda(n))*sin(Lambda(n)))/(Lambda(n)^3*(sin(2*Lambda(n))+2*Lambda(n))), n = 0 .. 99)

(18)
> 

S := eval(u(x, t) = Sum(3*exp(-(1/5)*Lambda(n)^2*t)*cos(Lambda(n)*x)*((-Lambda(n)^2+2)*cos(Lambda(n))-2+(Lambda(n)^3+2*Lambda(n))*sin(Lambda(n)))/(Lambda(n)^3*(sin(2*Lambda(n))+2*Lambda(n))), n = 0 .. 99), Sum = add)

> 

plot3d(rhs(S), x = 0 .. 1, t = 0 .. 1)

 

Compare with the numerical solution one could obtain using pdsolve with the numeric option . So substitute k = 1/5, and from the corresponding help page rewrite the initial/boundary conditions using D notation and this is the syntax and corresponding plot

> 

pds := pdsolve(subs(k = 1/5, pde), convert({iv}, D), numeric, time = t, range = 0 .. 1)

_m5021120320

(20)
> 

pds:-plot3d(t = 0 .. 1, x = 0 .. 1)

 
> 

``


 

Download PDE_and_BC_Example_with_RootOf_eigenvalues.mw

Edgardo S. Cheb-Terrab
Physics, Differential Equations and Mathematical Functions, Maplesoft

The first update to the Maple 2018 Physics, Differential Equations and Mathematical Functions packages is available. As has been the case since 2013, this update contains fixes, enhancements to existing functionality, and new developments in the three areas. 

The webpage for these updates will continue being the Maplesoft R&D Physics webpage. Starting with Maple 2018, however, this update is also available from the MapleCloud.

To install the update: open Maple and click the Cloud icon (upper-right corner), select "Packages" and search for "Physics Updates". Then, in the corresponding "Actions" column, click the third icon (install pop-up).

NOTE May/1: the "Updates" icon of the MapleCloud toolbar (that opens when you click the upper-right icon within a Maple document / worksheet), now works fine, after having installed the Physics Updates version 32 or higher.

These first updates include:

  • New Physics functionality regarding Tensor Products of Quantum States; and Coherent States.
  • Updates to pdsolve regarding PDE & Boundary Conditions (exact solutions);
  • A change in notation: d_(x), the differential of a coordinate in the Physics package, is now displayed as shown in this Mapleprimes post.


Edgardo S. Cheb-Terrab
Physics, Differential Equations and Mathematical Functions, Maplesoft

This is about the recent implementation of tensor products of quantum state spaces in the Physics package, in connection with an exchange with the Physics of Information Lab of the University of Waterloo. As usual this development is available to everybody from the Maplesoft R&D Physics webpage. This is the last update for Maple 2017. The updates for Maple 2018, starting with this same material, will begin being distributed through the MapleCloud next week.

Tensor Product of Quantum State Spaces

 

Basic ideas and design

 

 

Suppose A and B are quantum operators and Ket(A, n), et(B, m) are, respectively, their eigenkets. The following works since the introduction of the Physics package in Maple

> 

with(Physics)

> 

Setup(op = {A, B})

`* Partial match of  'op' against keyword 'quantumoperators'`

 

[quantumoperators = {A, B}]

(1)
> 

A*Ket(A, alpha) = A.Ket(A, alpha)

Physics:-`*`(A, Physics:-Ket(A, alpha)) = alpha*Physics:-Ket(A, alpha)

(2)
> 

B*Ket(B, beta) = B.Ket(B, beta)

Physics:-`*`(B, Physics:-Ket(B, beta)) = beta*Physics:-Ket(B, beta)

(3)

where on the left-hand sides the product operator `*` is used as a sort of inert form (it has all the correct mathematical properties but does not perform the contraction) of the dot product operator `.`, used on the right-hand sides.

 

Suppose now that A and B act on different, disjointed, Hilbert spaces.

 

1) To represent that, a new keyword in Setup , is introduced, to indicate which spaces are disjointed, say as in disjointedhilbertspaces = {A, B}.  We want this notation to pop up at some point as {`&Hscr;`[A], `&Hscr;`[B]} where the indexation indicates all the operators acting on that Hilbert space. The disjointedspaces keyword has for synonyms disjointedhilbertspaces and hilbertspaces. The display `&Hscr;`[A] is not yet implemented.

 

NOTE: noncommutative quantum operators acting on disjointed spaces commute between themselves, so after setting  - for instance - disjointedspaces = {A, B}, automatically, A, B become quantum operators satisfying (see comment (ii) on page 156 of ref. [1])

 

"[A,B][-]=0"

 

2) Product of Kets and Bras (KK, BB, KB and BK) where K and B belong to disjointed spaces, are understood as tensor products satisfying, for instance with disjointed spaces A and B (see footnote on page 154 of ref. [1]),

 

`&otimes;`(Ket(A, alpha), Ket(B, beta)) = `&otimes;`(Ket(B, beta), Ket(A, alpha)) 

 

`&otimes;`(Bra(A, alpha), Ket(B, beta)) = `&otimes;`(Ket(B, beta), Bra(A, alpha)) 

 

while of course

Bra(A, alpha)*Ket(A, alpha) <> Bra(A, alpha)*Ket(A, alpha)

 

Details

   

 

3) All the operators of one disjointed space act transparently over operators, Bras and Kets of the other disjointed spaces, for example

 

A*Ket(B, n) = A*Ket(B, n)

and the same for the Dagger of this equation, that is

Bra(B, n)*Dagger(A) = Bra(B, n)*Dagger(A)

 

And this happens automatically. Hence, when we write the left-hand sides and press enter, they are automatically rewritten (returned) as the right-hand sides.

 

Note that for the product of an operator times a Bra or a Ket we are not using the notation that expresses the product with the symbol 5.

 

Regarding the display of Bras and Kets and their tensor products, two enhancements are happening:

 

• 

A new Setup option hideketlabel makes all the labels in Kets and Bras to be hidden when displaying Kets, Bras and Bracket(s), with the indices presented one level up, as if they were a sequence of labels, so that:

 "Ket(A,m,n,l"  

is displayed as

Ket(A, m, n, l)

 

  

This is the notation used more frequently when working in quantum information. This hideketlabel option is already implemented entering Setup(hideketlabel = true)

• 

Tensor products formed with operators, or Bras and Kets, that belong to disjointed spaces (set as such using Setup ), are displayed with the symbol 5 in between, as in Ket(A, n)*Ket(B, n) instead of Ket(A, n)*Ket(B, n), and `&otimes;`(A, B) instead of A*B.

Tensor product notation and the hideketlabel option

   

The implementation of tensor products using `*` and `.`

   

Basic exercising with the new functionality

   

Related functionality already in place before these changes

   

Reference

 

[1] Cohen-Tannoudji, Diue, Laloe, Quantum Mechanics, Chapter 2, section F.

[2] Griffiths Robert B., Hilbert Space Quantum Mechanics, Quantum Computation and Quantum Information Theory Course, Physics Department, Carnegie Mellon University, 2014.

See also

   

 


 

Download TensorProductDesign.mw
 

Edgardo S. Cheb-Terrab
Physics, Differential Equations and Mathematical Functions, Maplesoft

 

Automatic handling of collision of tensor indices in products

 

 

The design of products of tensorial expressions that have contracted indices got enhanced. The idea: repeated indices in certain subexpressions are actually dummies. So suppose T[a, b] and B[b] are tensors, then in T[trace] = T[a, `~a`], a is just a dummy, therefore T[a, `~a`]*B[a] = T[b, `~b`]*B[a] is a well defined object. The new design automatically maps input like T[a, `~a`]*B[a] into T[b, `~b`]*B[a]. This significantly simplifies the manipulation of indices when working with products of tensorial expressions: each tensorial expression being multiplied has its repeated indices automatically transformed into different ones when they would collide with the free or repeated indices of the other expressions being multiplied.

 

This functionality is available within the Physics update distributed at the Maplesoft R&D Physics webpage (but for what you see under Preview that makes use of a new kernel feature of the Maple version under development).

 

> 

restart

> 

with(Physics); Setup(spacetimeindices = lowercaselatin, quiet)

[spacetimeindices = lowercaselatin]

(1)
> 

Define(T[a, b], B[b])

`Defined objects with tensor properties`

 

{B[b], Physics:-Dgamma[a], Physics:-Psigma[a], T[a, b], Physics:-d_[a], Physics:-g_[a, b], Physics:-KroneckerDelta[a, b], Physics:-LeviCivita[a, b, c, d]}

(2)

This shows the automatic handling of collision of indices

> 

T[a, a]*B[a]

T[b, `~b`]*B[a]

(3)
> 

T[a, a]^2

T[a, `~a`]*T[b, `~b`]

(4)
> 

``

Preview only in the upcomming version under development

 

Consider now the case of three tensors

> 

Define(A[a], C[a])

`Defined objects with tensor properties`

 

{A[a], B[b], C[a], Physics:-Dgamma[a], Physics:-Psigma[a], T[a, b], Physics:-d_[a], Physics:-g_[a, b], Physics:-KroneckerDelta[a, b], Physics:-LeviCivita[a, b, c, d]}

(5)
> 

A[a]*B[a]*C[a]

A[a]*B[a]*C[a]

(6)

The product above has indeed the index a repeated more than once, therefore none of its occurrences got automatically transformed into contravariant in the output, and Check  detects the problem interrupting with an error  message

> 

Check(A[a]*B[a]*C[a])

Error, (in Physics:-Check) wrong use of the summation rule for repeated indices: `a repeated 3 times`, in A[a]*B[a]*C[a]

 

 

However, it is now also possible to indicate, using parenthesis, that the product of two of these tensors actually form a subexpression, so that the following two tensorial expressions are well defined, and the colliding dummy is automatically replaced making that explicit

> 

A[a]*B[a]*C[a]

A[b]*B[`~b`]*C[a]

(7)
> 

A[a]*B[a]*C[a]

A[a]*B[b]*C[`~b`]

(8)

 

 

This change in design makes concretely simpler the use of indices in that it eliminates the need for manually replacing dummies. For example, consider the tensorial expression for the angular momentum in terms of the coordinates and momentum vectors, in 3 dimensions

 

> 

Setup(coordinates = cartesian, dimension = 3, metric = euclidean, quiet)

[coordinatesystems = {X}, dimension = 3, metric = {(1, 1) = 1, (2, 2) = 1, (3, 3) = 1}]

(9)

Define L[j], p[k] respectively representing angular and linear momentum

> 

Define(L[j], p[k])

`Defined objects with tensor properties`

 

{Physics:-Dgamma[a], L[j], Physics:-Psigma[a], Physics:-d_[a], Physics:-g_[a, b], p[k], Physics:-KroneckerDelta[a, b], Physics:-LeviCivita[a, b, c], Physics:-SpaceTimeVector[a](X)}

(10)

Introduce the tensorial expression for L[a]

> 

L[a] = LeviCivita[a, b, c]*X[b]*p[c]

L[a] = Physics:-LeviCivita[a, b, c]*Physics:-SpaceTimeVector[b](X)*p[c]

(11)

The left-hand side has one free index, a, while the right-hand side has two dummy indices b and c

> 

Check(L[a] = Physics[LeviCivita][a, b, c]*Physics[SpaceTimeVector][b](X)*p[c], all)

`The repeated indices per term are: `[{`...`}, {`...`}, `...`]*`; the free indices are: `*{`...`}

 

([{}], {a}) = ([{b, c}], {a})

(12)

If we want to compute`#mrow(msup(mfenced(mover(mi("L"),mo("&rarr;")),open = "&Vert;",close = "&Vert;"),mn("2")),mo("&equals;"),msubsup(mi("L"),mi("a"),mn("2")))`we can now take the square of (11) directly, and the dummy indices on the right-hand side are automatically handled, there is now no need to manually substitute the repeated indices to avoid their collision

> 

(L[a] = Physics[LeviCivita][a, b, c]*Physics[SpaceTimeVector][b](X)*p[c])^2

L[a]^2 = Physics:-LeviCivita[a, b, c]*Physics:-SpaceTimeVector[b](X)*p[c]*Physics:-LeviCivita[a, d, e]*Physics:-SpaceTimeVector[d](X)*p[e]

(13)

The repeated indices on the right-hand side are now a, b, c, d, e

> 

Check(L[a]^2 = Physics[LeviCivita][a, b, c]*Physics[SpaceTimeVector][b](X)*p[c]*Physics[LeviCivita][a, d, e]*Physics[SpaceTimeVector][d](X)*p[e], all)

`The repeated indices per term are: `[{`...`}, {`...`}, `...`]*`; the free indices are: `*{`...`}

 

([{a}], {}) = ([{a, b, c, d, e}], {})

(14)
> 

NULL


 

Download AutomaticHandlingCollisionOfTensorIndices.mw

Edgardo S. Cheb-Terrab
Physics, Differential Equations and Mathematical Functions, Maplesoft

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