janhardo

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11 years, 314 days
B. Ed math

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bol_waaruit_intrisiek_iets_wordt_afgeleid_17-7-2026BELANGRIJK.mw

Some own exercises to get a impression of diffferentilal geometry 

@Harry Garst 
Ja , en hopelijk kan je er iets mee met de module code :-)
Nog wat verder uitbreiden met nieuwe regels 
groet Jan

@janhardo 
Testing commando's 
Performance test.comparison, seems to be quick with the module

with(LinearAlgebra):

# matrix size (adjust as needed!)
n := 40:

# random matrices
A := RandomMatrix(n,n, generator=-5..5):
B := RandomMatrix(n,n, generator=-5..5):
C := RandomMatrix(n,n, generator=-5..5):
d := RandomMatrix(n,n, generator=-5..5):

# ---------- 1. Naive method ----------
t1 := time():

K1 := KroneckerProduct(A,B):
K2 := KroneckerProduct(C,d):
R1 := K1 . K2:

t_naive := time() - t1:

# ---------- 2. Smart method (KRON7) ----------
t2 := time():

R2 := KroneckerProduct(A . C, B . d):

t_smart := time() - t2:

# ---------- Results ----------
printf("Naive time  = %.4f sec\n", t_naive);
printf("Smart time  = %.4f sec\n", t_smart);
printf("Speedup(faster)     = %.2f x\n", t_naive / t_smart);

# ---------- Verification ----------
printf("Equal? %a\n", Equal(R1, R2));
Naive time  = 244.7970 sec
Smart time  = 1.2500 sec
Speedup(faster)     = 195.84 x
Equal? true

restart;
with(LinearAlgebra):
ZehfussReplace := proc(expr::uneval)
    uses LinearAlgebra:
    local replace;
    replace := proc(det)
        local kr_args, A, B;
        kr_args := op(1, det);
        if not type(kr_args, specfunc(KroneckerProduct)) then return eval(det) end if;
        A := op(1, kr_args);
        B := op(2, kr_args);
        'Determinant'(A)^(RowDimension(B)) * 'Determinant'(B)^(RowDimension(A));
    end proc;
    subsindets(expr, specfunc(specfunc(KroneckerProduct), Determinant), replace);
end proc:

A := Matrix(2,2,symbol=a);
B := Matrix(3,3,symbol=b);

# Important: call the procedure directly with the unevaluated form
result := ZehfussReplace( Determinant(KroneckerProduct(A,B)) + 2*Determinant(KroneckerProduct(A,A)) );
# print(result);
  eval(result);

Matrix(2, 2, {(1, 1) = a[1, 1], (1, 2) = a[1, 2], (2, 1) = a[2, 1], (2, 2) = a[2, 2]})

 

Matrix(%id = 36893490552170632124)

 

LinearAlgebra:-Determinant(A)^3*LinearAlgebra:-Determinant(B)^2+2*LinearAlgebra:-Determinant(A)^4

 

(a[1, 1]*a[2, 2]-a[1, 2]*a[2, 1])^3*(b[1, 1]*b[2, 2]*b[3, 3]-b[1, 1]*b[2, 3]*b[3, 2]-b[1, 2]*b[2, 1]*b[3, 3]+b[1, 2]*b[2, 3]*b[3, 1]+b[1, 3]*b[2, 1]*b[3, 2]-b[1, 3]*b[2, 2]*b[3, 1])^2+2*(a[1, 1]*a[2, 2]-a[1, 2]*a[2, 1])^4

(1)
 

 

Download Zehfuss_-kronecker_determinant__mprimes_22-4-2026.mw

@Harry Garst 

It doesn't seem necessary to use two variables.

These are two ways of describing the same variable..

Apparently, the expression hold for all n =1.. 2
proof by induction further

 

Of all closed surfaces in space with a fixed surface area A​, which surface encloses the largest volume?

But there are two variables in the functional here.

a good example to to do by hand...

Via differential geometry 
 

 

 

 

restart;
Typesetting:-Settings(typesetdot=true);

printf("================================================================\n");
printf("ISOPERIMETRIC PROBLEM: Maximize area for fixed perimeter\n");
printf("================================================================\n\n");
printf("Goal: Prove that the optimal closed curve is a circle.\n");
printf("We do this by showing that its curvature must be constant.\n\n");

# Step 1: Lagrangian
F := x(t)*diff(y(t),t) + lambda*sqrt(diff(x(t),t)^2 + diff(y(t),t)^2);
printf("Step 1: Lagrangian F = %a\n\n", F);

# Step 2: Euler-Lagrange equations
printf("Step 2: Euler-Lagrange equations\n");
EL1 := diff(Physics:-diff(F, diff(x(t),t)), t) - Physics:-diff(F, x(t)) = 0;
EL2 := diff(Physics:-diff(F, diff(y(t),t)), t) - Physics:-diff(F, y(t)) = 0;
printf("EL1: %a\n", EL1);
printf("EL2: %a\n\n", EL2);

# Step 3: First integrals (integrate once)
printf("Step 3: First integrals\n");
eq1 := lambda*diff(x(t),t)/sqrt(diff(x(t),t)^2+diff(y(t),t)^2) = y(t) + C1;
eq2 := x(t) + lambda*diff(y(t),t)/sqrt(diff(x(t),t)^2+diff(y(t),t)^2) = C2;
printf("First integral I:  %a\n", eq1);
printf("First integral II: %a\n\n", eq2);

# Step 4: Switch to arc length parameter s
printf("Step 4: Arc length parametrization (ds/dt = sqrt(x'^2+y'^2))\n");
printf("Then dx/ds = x'/(ds/dt), dy/ds = y'/(ds/dt)\n");
printf("The first integrals become:\n");
printf("   lambda * dx/ds = y + C1      (A)\n");
printf("   lambda * dy/ds = C2 - x      (B)\n\n");

# Step 5: Compute curvature explicitly
printf("Step 5: Curvature calculation\n");
printf("For a plane curve, curvature kappa = (dx/ds)*(d^2y/ds^2) - (dy/ds)*(d^2x/ds^2)\n");
printf("From (A) and (B) we obtain:\n");

dx_ds := (y + C1)/lambda;
dy_ds := (C2 - x)/lambda;
printf("   dx/ds = %a\n", dx_ds);
printf("   dy/ds = %a\n", dy_ds);

d2x_ds2 := (C2 - x)/lambda^2;
d2y_ds2 := -(y + C1)/lambda^2;
printf("   d^2x/ds^2 = %a\n", d2x_ds2);
printf("   d^2y/ds^2 = %a\n\n", d2y_ds2);

kappa := dx_ds * d2y_ds2 - dy_ds * d2x_ds2;
kappa_expanded := expand(kappa);
printf("Substituting into the curvature formula and expanding:\n");
printf("   kappa = %a\n", kappa_expanded);
printf("Thus kappa = -(C2-x)^2/lambda^3 - (y+C1)^2/lambda^3\n\n");

# Step 6: Use the arc length normalization
printf("Step 6: Arc length normalization\n");
printf("Since (dx/ds)^2 + (dy/ds)^2 = 1, from (A) and (B):\n");
printf("   ((y+C1)/lambda)^2 + ((C2-x)/lambda)^2 = 1\n");
printf("   => (y+C1)^2 + (C2-x)^2 = lambda^2\n");
norm_eq := (y+C1)^2 + (C2-x)^2 = lambda^2;
printf("Hence: %a\n\n", norm_eq);

# Compact simplification of kappa_final
printf("Step 6a: Compact simplification of curvature\n");
# Substitute the sum directly
kappa_final := algsubs((y+C1)^2+(C2-x)^2 = lambda^2, kappa_expanded);
printf("   kappa = %a\n", kappa_final);
printf("Therefore the curvature is constant: kappa = -1/lambda.\n");
printf("This proves that the optimal curve has constant curvature.\n\n");

# Step 7: Conclusion – circle
printf("Step 7: Conclusion\n");
printf("A plane curve with constant nonzero curvature is an arc of a circle.\n");
printf("Since the curve is closed and smooth, it must be a full circle.\n");
printf("The radius is R = |lambda|, and the center is (C2, -C1).\n");
printf("Thus the optimal shape is a circle.\n\n");

# Step 8: Example plot
printf("Step 8: Example plot (lambda=1, C1=0, C2=0)\n");
lambda_val := 1; C1_val := 0; C2_val := 0; phi := 0;
x_circ := t -> C2_val + lambda_val*cos(2*Pi*t + phi);
y_circ := t -> -C1_val + lambda_val*sin(2*Pi*t + phi);
plot([x_circ(t), y_circ(t), t=0..1], scaling=constrained,
     title="Optimal closed curve: circle", labels=["x","y"],
     color=blue, thickness=2);
printf("Plot displayed.\n");

false

 

================================================================
ISOPERIMETRIC PROBLEM: Maximize area for fixed perimeter
================================================================

Goal: Prove that the optimal closed curve is a circle.
We do this by showing that its curvature must be constant.

 

 

x(t)*(diff(y(t), t))+lambda*((diff(x(t), t))^2+(diff(y(t), t))^2)^(1/2)

 

Step 1: Lagrangian F = x(t)*diff(y(t),t)+lambda*(diff(x(t),t)^2+diff(y(t),t)^2)^(1/2)

Step 2: Euler-Lagrange equations

 

-(1/2)*lambda*(diff(x(t), t))*(2*(diff(x(t), t))*(diff(diff(x(t), t), t))+2*(diff(y(t), t))*(diff(diff(y(t), t), t)))/((diff(x(t), t))^2+(diff(y(t), t))^2)^(3/2)+lambda*(diff(diff(x(t), t), t))/((diff(x(t), t))^2+(diff(y(t), t))^2)^(1/2)-(diff(y(t), t)) = 0

 

diff(x(t), t)-(1/2)*lambda*(diff(y(t), t))*(2*(diff(x(t), t))*(diff(diff(x(t), t), t))+2*(diff(y(t), t))*(diff(diff(y(t), t), t)))/((diff(x(t), t))^2+(diff(y(t), t))^2)^(3/2)+lambda*(diff(diff(y(t), t), t))/((diff(x(t), t))^2+(diff(y(t), t))^2)^(1/2) = 0

 

EL1: -1/2*lambda/(diff(x(t),t)^2+diff(y(t),t)^2)^(3/2)*diff(x(t),t)*(2*diff(x(t),t)*diff(diff(x(t),t),t)+2*diff(y(t),t)*diff(diff(y(t),t),t))+lambda/(diff(x(t),t)^2+diff(y(t),t)^2)^(1/2)*diff(diff(x(t),t),t)-diff(y(t),t) = 0
EL2: diff(x(t),t)-1/2*lambda/(diff(x(t),t)^2+diff(y(t),t)^2)^(3/2)*diff(y(t),t)*(2*diff(x(t),t)*diff(diff(x(t),t),t)+2*diff(y(t),t)*diff(diff(y(t),t),t))+lambda/(diff(x(t),t)^2+diff(y(t),t)^2)^(1/2)*diff(diff(y(t),t),t) = 0

Step 3: First integrals

 

lambda*(diff(x(t), t))/((diff(x(t), t))^2+(diff(y(t), t))^2)^(1/2) = y(t)+C1

 

x(t)+lambda*(diff(y(t), t))/((diff(x(t), t))^2+(diff(y(t), t))^2)^(1/2) = C2

 

First integral I:  lambda/(diff(x(t),t)^2+diff(y(t),t)^2)^(1/2)*diff(x(t),t) = y(t)+C1
First integral II: x(t)+lambda/(diff(x(t),t)^2+diff(y(t),t)^2)^(1/2)*diff(y(t),t) = C2

Step 4: Arc length parametrization (ds/dt = sqrt(x'^2+y'^2))
Then dx/ds = x'/(ds/dt), dy/ds = y'/(ds/dt)
The first integrals become:
   lambda * dx/ds = y + C1      (A)
   lambda * dy/ds = C2 - x      (B)

Step 5: Curvature calculation
For a plane curve, curvature kappa = (dx/ds)*(d^2y/ds^2) - (dy/ds)*(d^2x/ds^2)
From (A) and (B) we obtain:

 

(y+C1)/lambda

 

(C2-x)/lambda

 

   dx/ds = (y+C1)/lambda
   dy/ds = (C2-x)/lambda

 

(C2-x)/lambda^2

 

-(y+C1)/lambda^2

 

   d^2x/ds^2 = (C2-x)/lambda^2
   d^2y/ds^2 = -(y+C1)/lambda^2

 

 

-(C2-x)^2/lambda^3-(y+C1)^2/lambda^3

 

-C2^2/lambda^3+2*C2*x/lambda^3-x^2/lambda^3-C1^2/lambda^3-2*y*C1/lambda^3-y^2/lambda^3

 

Substituting into the curvature formula and expanding:
   kappa = -1/lambda^3*C2^2+2/lambda^3*C2*x-1/lambda^3*x^2-1/lambda^3*C1^2-2/lambda^3*y*C1-1/lambda^3*y^2
Thus kappa = -(C2-x)^2/lambda^3 - (y+C1)^2/lambda^3

Step 6: Arc length normalization
Since (dx/ds)^2 + (dy/ds)^2 = 1, from (A) and (B):
   ((y+C1)/lambda)^2 + ((C2-x)/lambda)^2 = 1
   => (y+C1)^2 + (C2-x)^2 = lambda^2

 

(y+C1)^2+(C2-x)^2 = lambda^2

 

Hence: (y+C1)^2+(C2-x)^2 = lambda^2

Step 6a: Compact simplification of curvature

 

-1/lambda

 

   kappa = -1/lambda
Therefore the curvature is constant: kappa = -1/lambda.
This proves that the optimal curve has constant curvature.

Step 7: Conclusion
A plane curve with constant nonzero curvature is an arc of a circle.
Since the curve is closed and smooth, it must be a full circle.
The radius is R = |lambda|, and the center is (C2, -C1).
Thus the optimal shape is a circle.

Step 8: Example plot (lambda=1, C1=0, C2=0)

 

1

 

0

 

0

 

0

 

proc (t) options operator, arrow; C2_val+lambda_val*cos(2*Pi*t+phi) end proc

 

proc (t) options operator, arrow; -C1_val+lambda_val*sin(2*Pi*t+phi) end proc

 

 

Plot displayed.

 

 

via vectorcalculus 
bewijs_cirkel_gesloten_cvorm_viavectot_calculus_mprimes_20-4-2026.mw

Download bewijs_cirkel_gesloten_cvorm_via_diff_geom_mprimes_20-4-2026.mw

@Rouben Rostamian  
 

Thanks for your educational code , yes, this circle is a  interesting example . I had actually seen another derivation involving the curvature of the circle, but that leads into vector calculus and differential geometry, which require more advanced knowledge.

It’s also instructive to see different approaches to solving the problem.

Of all closed curves with a fixed length L, which curve encloses the largest area?


 

 

 

 

restart;
with(plots):

# ============================================================
# NONLINEAR WAVE EQUATION SOLVED AS COUPLED ODEs
# ============================================================
#
# ORIGINAL PDE: ∂²u/∂t² = c²(λ(H + ∂u/∂x)) · ∂²u/∂x²
#
# AFTER SPATIAL DISCRETIZATION (FINITE DIFFERENCES):
# We obtain a SYSTEM of COUPLED ORDINARY DIFFERENTIAL EQUATIONS (ODEs):
#
# For each internal point i = 1, 2, ..., Nx-1:
#
#   d²u_i/dt² = c²(λ(H + (u_{i+1}-u_{i-1})/(2Δx))) · (u_{i+1}-2u_i+u_{i-1})/Δx²
#
# This is a system of (Nx-1) coupled, nonlinear ODEs.
# ============================================================

# ============================================================
# STEP 1: PHYSICAL SYSTEM PARAMETERS
# ============================================================
L := 0.04;                         # Length of the rod [m] (4 cm)
A0 := 12*L/2*10^(-6);              # Initial amplitude [m] (~2.4e-7 m)
Nx := 100;                         # Number of spatial points (creates 99 ODEs)
dx := L/Nx;                        # Spatial step size [m]
x_vals := [seq(i*dx, i=0..Nx)];    # Positions along the rod [m]

printf("========================================\n");
printf("     COUPLED ODEs FROM WAVE EQUATION\n");
printf("========================================\n\n");
printf("STEP 1: SYSTEM PARAMETERS\n");
printf("  Rod length L = %.4f m (%.1f cm)\n", L, L*100);
printf("  Initial amplitude A0 = %.2e m\n", A0);
printf("  Number of spatial points Nx+1 = %d\n", Nx+1);
printf("  Number of COUPLED ODEs = %d (for internal points)\n", Nx-1);
printf("  Spatial step dx = %.2e m\n\n", dx);

# ============================================================
# STEP 2: MATERIAL MODEL (PIECEWISE FUNCTIONS)
# ============================================================
# c_sq(H) = squared speed of sound [m²/s²]
c_sq := proc(H)
    if H < 700 then
        return -3143*H + 1.99e7;      # Region 1: decreasing speed
    elif H < 1000 then
        return 7333*H + 1.257e7;      # Region 2: increasing speed
    else
        return 1.99e7;                # Region 3: constant speed
    end if;
end proc:

# lambda(H) = helper function for inverse relation
lambda := proc(H)
    if H < 1000 then
        return (3/250000000)*H;
    else
        return 3/250000;
    end if;
end proc:

printf("STEP 2: MATERIAL MODEL\n");
printf("  c²(H) = piecewise function:\n");
printf("    H < 700:   c² = -3143·H + 1.99e7\n");
printf("    700 ≤ H < 1000: c² = 7333·H + 1.257e7\n");
printf("    H ≥ 1000:  c² = 1.99e7 (constant)\n");
printf("  λ(H) = 3/250000000 * H for H < 1000\n\n");

# ============================================================
# STEP 3: NUMERICAL PARAMETERS
# ============================================================
c_max := sqrt(1.99e7);              # Maximum wave speed [m/s]
dt := 0.5 * dx / c_max;             # Time step (CFL = 0.5 for stability)
f0 := sqrt(c_sq(650))/(2*L);        # Resonance frequency [Hz]
T0 := 1/f0;                         # Period [s]
Nt := 1500;                         # Number of time steps

printf("STEP 3: NUMERICAL PARAMETERS\n");
printf("  Maximum wave speed c_max = %.1f m/s\n", c_max);
printf("  Resonance frequency f0 = %.1f Hz (ultrasonic!)\n", f0);
printf("  Period T0 = %.2e s\n", T0);
printf("  Time step dt = %.2e s\n", dt);
printf("  Number of time steps Nt = %d\n", Nt);
printf("  CFL number = %.3f (must be < 1 for stability)\n\n", c_max*dt/dx);

# ============================================================
# STEP 4: INITIALIZE THE SOLUTION (Three time layers)
# ============================================================
# We need three arrays for the time integration:
#   u_old = solution at time t - dt
#   u_curr = solution at time t
#   u_new = solution at time t + dt

u_old := Array(0..Nx);
u_curr := Array(0..Nx);
u_new := Array(0..Nx);

# INITIAL CONDITION: u(x,0) = A0·cos(πx/L)
# This is the FIRST HARMONIC vibration mode
for i from 0 to Nx do
    u_curr[i] := A0 * cos(Pi * x_vals[i+1] / L);
    u_old[i] := u_curr[i];         # Initial velocity = 0 => u_old = u_curr
end do:

printf("STEP 4: INITIAL CONDITION\n");
printf("  u(x,0) = A0·cos(πx/L)\n");
printf("  ∂u/∂t(x,0) = 0\n");
printf("  This creates the first harmonic vibration mode.\n");
printf("  The rod is maximally stretched at the middle (x = %.2f m).\n\n", L/2);

# Display the initial values for the first few points (educational)
printf("  Initial values for the first 5 points (i=0 to 4):\n");
for i from 0 to 4 do
    printf("    u[%d](0) = %.2e m at x = %.4f m\n", i, u_curr[i], x_vals[i+1]);
end do;
printf("    ...\n\n");

# ============================================================
# STEP 5: BOUNDARY CONDITIONS (Neumann - Free Ends)
# ============================================================
# Neumann: ∂u/∂x = 0 at both ends
# This means: u[0] = u[1] and u[Nx] = u[Nx-1]
# Physically: The ends are FREE (no force)
apply_bc := proc()
    u_new[0] := u_new[1];
    u_new[Nx] := u_new[Nx-1];
end proc:

printf("STEP 5: BOUNDARY CONDITIONS\n");
printf("  Neumann: ∂u/∂x = 0 at x = 0 and x = L\n");
printf("  This means: u(t) = u(t) and u_%d(t) = u_%d(t)\n", Nx, Nx-1);
printf("  Physically: The ends are FREE (can move without force).\n");
printf("  Waves reflect at the ends WITHOUT sign reversal.\n\n");

# ============================================================
# STEP 6: THE COUPLED ODE SYSTEM - EXPLANATION
# ============================================================
printf("STEP 6: THE COUPLED ODE SYSTEM\n");
printf("  After spatial discretization, we get %d coupled ODEs:\n\n", Nx-1);

for i from 1 to 3 do
    printf("  ODE %d: d²u_%d/dt² = c²(λ(H + (u_%d-u_%d)/(2dx))) · (u_%d-2u_%d+u_%d)/dx²\n",
           i, i, i+1, i-1, i+1, i, i-1);
end do;
printf("  ...\n");
for i from Nx-3 to Nx-1 do
    printf("  ODE %d: d²u_%d/dt² = c²(λ(H + (u_%d-u_%d)/(2dx))) · (u_%d-2u_%d+u_%d)/dx²\n",
           i, i, i+1, i-1, i+1, i, i-1);
end do;
printf("\n  Each ODE connects point i with its neighbors i-1 and i+1.\n");
printf("  This is why they are called COUPLED ODEs.\n\n");

# ============================================================
# STEP 7: SOLVING THE COUPLED ODEs USING FINITE DIFFERENCES
# ============================================================
# We use an explicit time integration scheme:
#   u_new[i] = 2*u_curr[i] - u_old[i] + dt² · (d²u_i/dt²)
#
# This comes from the central difference approximation:
#   d²u_i/dt² ≈ (u_new[i] - 2*u_curr[i] + u_old[i]) / dt²

printf("STEP 7: SOLVING THE COUPLED ODEs\n");
printf("  Using explicit time integration (central differences):\n");
printf("    u_new[i] = 2*u_curr[i] - u_old[i] + dt² · (d²u_i/dt²)\n\n");
printf("  Starting numerical integration...\n\n");

# Storage indices for visualization
save_indices := [42, 106, 211, 317, 422];
solutions := table();

# TIME INTEGRATION LOOP
for n from 1 to Nt do
    
    # --------------------------------------------------------
    # COMPUTE ALL COUPLED ODEs
    # Each iteration of this loop computes ONE ODE for point i
    # The ODEs are COUPLED because u_new[i] depends on u_curr[i-1] and u_curr[i+1]
    # --------------------------------------------------------
    for i from 1 to Nx-1 do
        
        # ----- COUPLING TERM 1: LOCAL STRAIN (∂u/∂x) -----
        # This connects u_i with its neighbors u_{i-1} and u_{i+1}
        du_dx := (u_curr[i+1] - u_curr[i-1]) / (2 * dx);
        
        # ----- MATERIAL PARAMETER H DEPENDS ON STRAIN -----
        H_total := 650 + du_dx;
        
        # ----- SQUARED SPEED OF SOUND c² -----
        if H_total < 700 then
            c2 := -3143*H_total + 1.99e7;
        elif H_total < 1000 then
            c2 := 7333*H_total + 1.257e7;
        else
            c2 := 1.99e7;
        end if;
        
        # ----- COUPLING TERM 2: SECOND DERIVATIVE (∂²u/∂x²) -----
        # This also connects u_i with its neighbors u_{i-1} and u_{i+1}
        u_xx := (u_curr[i+1] - 2*u_curr[i] + u_curr[i-1]) / dx^2;
        
        # ----- THE COUPLED ODE FOR POINT i -----
        # d²u_i/dt² = c² · u_xx
        # Discretized in time:
        u_new[i] := 2*u_curr[i] - u_old[i] + dt^2 * c2 * u_xx;
        
    end do;
    # --------------------------------------------------------
    # END OF COUPLED ODE COMPUTATION
    # --------------------------------------------------------
    
    # Apply boundary conditions (coupling the end points)
    apply_bc();
    
    # Store solutions for visualization
    for idx from 1 to 5 do
        if n = save_indices[idx] then
            solutions[n] := copy(u_new);
            printf("  Stored: t = %.3f T0 (time step %d/%d)\n", n*dt/T0, n, Nt);
        end if;
    end do;
    
    # Shift time layers for next iteration
    for i from 0 to Nx do
        u_old[i] := u_curr[i];
        u_curr[i] := u_new[i];
    end do;
    
    # Progress indicator
    if n mod 500 = 0 then
        printf("  Progress: %d/%d (%.0f%%)\n", n, Nt, 100*n/Nt);
    end if;
end do:

printf("\nSimulation completed!\n\n");

# ============================================================
# STEP 8: VISUALIZATION OF THE COUPLED ODE SOLUTION
# ============================================================
printf("STEP 8: VISUALIZATION\n");
printf("  Generating plots of the solution u_i(t)...\n");

colors := ["red", "blue", "green", "magenta", "black"];
plot_list := [];

for idx from 1 to 5 do
    n_idx := save_indices[idx];
    t_val := evalf(n_idx * dt / T0, 3);
    points := [seq([x_vals[i+1], solutions[n_idx][i]], i=0..Nx)];
    p := plot(points,
              labels=["x [m]", "u(x,t) [m]"],
              title=sprintf("Wave at t = %.3f T", t_val),
              color=colors[idx],
              legend=sprintf("t = %.3f T", t_val),
              thickness=2);
    plot_list := [op(plot_list), p];
end do:

display(plot_list, title="Solution of the Coupled ODE System", size=[800,500]);
printf("  Plot shows the wave at 5 different times.\n\n");

# ============================================================
# STEP 9: ANIMATION OF THE COUPLED ODE SOLUTION
# ============================================================
printf("STEP 9: ANIMATION\n");
printf("  Generating animation of the moving wave...\n");

animation_frames := [];
t_labels := [0.1, 0.25, 0.5, 0.75, 1.0];
explanation := [
    "t = 0.10 T: Wave begins moving right.\nThe wave deforms because c² depends on strain.",
    "t = 0.25 T: Wave approaches the right end.\nAmplitude changes due to nonlinearity.",
    "t = 0.50 T: Wave reaches the end and reflects.\n∂u/∂x=0 at the end (free boundary).",
    "t = 0.75 T: Wave moves back left.\nWave shape is mirrored.",
    "t = 1.00 T: Wave returns to initial condition.\nOne complete period completed."
];

for idx from 1 to 5 do
    n_idx := save_indices[idx];
    t_val := evalf(n_idx * dt / T0, 3);
    points := [seq([x_vals[i+1], solutions[n_idx][i]], i=0..Nx)];
    
    p := plot(points,
              labels=["x [m]", "u(x,t) [m]"],
              title=sprintf("Longitudinal wave at t = %.2f T", t_labels[idx]),
              color="blue",
              thickness=3,
              view=[0..L, -A0*2..A0*2]);
    
    text := plots:-textplot([0.02, A0*1.5, explanation[idx]],
                            font=["Arial", 10],
                            color="darkred",
                            align="LEFT");
    
    animation_frames := [op(animation_frames), display(p, text)];
end do:

# Repeat frames for smooth animation
smooth_frames := [];
for rep from 1 to 4 do
    for idx from 1 to 5 do
        smooth_frames := [op(smooth_frames), animation_frames[idx]];
    end do;
end do:

display(smooth_frames, insequence=true, size=[800,600]);
printf("  Animation started! (%d frames)\n\n", nops(smooth_frames));

# ============================================================
# STEP 10: SUMMARY FOR THE STUDENT
# ============================================================
printf("========================================\n");
printf("              SUMMARY\n");
printf("========================================\n\n");
printf("WHAT IS A COUPLED ODE SYSTEM?\n");
printf("  • We have %d unknown functions u_i(t), i = 1..%d\n", Nx-1, Nx-1);
printf("  • Each ODE connects u_i with its neighbors u_{i-1} and u_{i+1}\n");
printf("  • This is why they are called COUPLED ODEs\n\n");
printf("WHERE ARE THE ODEs IN THE CODE?\n");
printf("  • Inside the main time loop (for n from 1 to Nt)\n");
printf("  • In the inner loop (for i from 1 to Nx-1)\n");
printf("  • Each iteration computes ONE ODE for point i\n\n");
printf("WHAT DOES EACH ODE DO?\n");
printf("  • Computes d²u_i/dt² = c² · (u_{i+1}-2u_i+u_{i-1})/dx²\n");
printf("  • Updates u_new[i] = 2*u_curr[i] - u_old[i] + dt² · (d²u_i/dt²)\n\n");
printf("ANSWER TO THE ORIGINAL QUESTION:\n");
printf("  Q: Can pdsolve handle c²(λ(H + ∂u/∂x))?\n");
printf("  A: NO, pdsolve CANNOT handle this nonlinear PDE.\n");
printf("  SOLUTION: Convert to COUPLED ODEs and use finite differences.\n");
printf("========================================\n");

0.4e-1

 

0.2400000000e-6

 

100

 

0.4000000000e-3

 

[0., 0.4000000000e-3, 0.8000000000e-3, 0.1200000000e-2, 0.1600000000e-2, 0.2000000000e-2, 0.2400000000e-2, 0.2800000000e-2, 0.3200000000e-2, 0.3600000000e-2, 0.4000000000e-2, 0.4400000000e-2, 0.4800000000e-2, 0.5200000000e-2, 0.5600000000e-2, 0.6000000000e-2, 0.6400000000e-2, 0.6800000000e-2, 0.7200000000e-2, 0.7600000000e-2, 0.8000000000e-2, 0.8400000000e-2, 0.8800000000e-2, 0.9200000000e-2, 0.9600000000e-2, 0.1000000000e-1, 0.1040000000e-1, 0.1080000000e-1, 0.1120000000e-1, 0.1160000000e-1, 0.1200000000e-1, 0.1240000000e-1, 0.1280000000e-1, 0.1320000000e-1, 0.1360000000e-1, 0.1400000000e-1, 0.1440000000e-1, 0.1480000000e-1, 0.1520000000e-1, 0.1560000000e-1, 0.1600000000e-1, 0.1640000000e-1, 0.1680000000e-1, 0.1720000000e-1, 0.1760000000e-1, 0.1800000000e-1, 0.1840000000e-1, 0.1880000000e-1, 0.1920000000e-1, 0.1960000000e-1, 0.2000000000e-1, 0.2040000000e-1, 0.2080000000e-1, 0.2120000000e-1, 0.2160000000e-1, 0.2200000000e-1, 0.2240000000e-1, 0.2280000000e-1, 0.2320000000e-1, 0.2360000000e-1, 0.2400000000e-1, 0.2440000000e-1, 0.2480000000e-1, 0.2520000000e-1, 0.2560000000e-1, 0.2600000000e-1, 0.2640000000e-1, 0.2680000000e-1, 0.2720000000e-1, 0.2760000000e-1, 0.2800000000e-1, 0.2840000000e-1, 0.2880000000e-1, 0.2920000000e-1, 0.2960000000e-1, 0.3000000000e-1, 0.3040000000e-1, 0.3080000000e-1, 0.3120000000e-1, 0.3160000000e-1, 0.3200000000e-1, 0.3240000000e-1, 0.3280000000e-1, 0.3320000000e-1, 0.3360000000e-1, 0.3400000000e-1, 0.3440000000e-1, 0.3480000000e-1, 0.3520000000e-1, 0.3560000000e-1, 0.3600000000e-1, 0.3640000000e-1, 0.3680000000e-1, 0.3720000000e-1, 0.3760000000e-1, 0.3800000000e-1, 0.3840000000e-1, 0.3880000000e-1, 0.3920000000e-1, 0.3960000000e-1, 0.4000000000e-1]

 

========================================
     COUPLED ODEs FROM WAVE EQUATION
========================================

STEP 1: SYSTEM PARAMETERS
  Rod length L = 0.0400 m (4.0 cm)
  Initial amplitude A0 = 2.40e-07 m
  Number of spatial points Nx+1 = 101
  Number of COUPLED ODEs = 99 (for internal points)
  Spatial step dx = 4.00e-04 m

STEP 2: MATERIAL MODEL
  c²(H) = piecewise function:
    H < 700:   c² = -3143·H + 1.99e7
    700 ≤ H < 1000: c² = 7333·H + 1.257e7
    H ≥ 1000:  c² = 1.99e7 (constant)
  λ(H) = 3/250000000 * H for H < 1000

 

 

4460.941605

 

0.4483358396e-7

 

52822.00360

 

0.1893150452e-4

 

1500

 

STEP 3: NUMERICAL PARAMETERS
  Maximum wave speed c_max = 4460.9 m/s
  Resonance frequency f0 = 52822.0 Hz (ultrasonic!)
  Period T0 = 1.89e-05 s
  Time step dt = 4.48e-08 s
  Number of time steps Nt = 1500
  CFL number = 0.500 (must be < 1 for stability)

 

 

`Array(0..100, {(1) = 0, (2) = 0, (3) = 0, (4) = 0, (5) = 0, (6) = 0, (7) = 0, (8) = 0, (9) = 0, (10) = 0, (11) = 0, (12) = 0, (13) = 0, (14) = 0, (15) = 0, (16) = 0, (17) = 0, (18) = 0, (19) = 0, (20) = 0, (21) = 0, (22) = 0, (23) = 0, (24) = 0, (25) = 0, (26) = 0, (27) = 0, (28) = 0, (29) = 0, (30) = 0, (31) = 0, (32) = 0, (33) = 0, (34) = 0, (35) = 0, (36) = 0, (37) = 0, (38) = 0, (39) = 0, (40) = 0, (41) = 0, (42) = 0, (43) = 0, (44) = 0, (45) = 0, (46) = 0, (47) = 0, (48) = 0, (49) = 0, (50) = 0, (51) = 0, (52) = 0, (53) = 0, (54) = 0, (55) = 0, (56) = 0, (57) = 0, (58) = 0, (59) = 0, (60) = 0, (61) = 0, (62) = 0, (63) = 0, (64) = 0, (65) = 0, (66) = 0, (67) = 0, (68) = 0, (69) = 0, (70) = 0, (71) = 0, (72) = 0, (73) = 0, (74) = 0, (75) = 0, (76) = 0, (77) = 0, (78) = 0, (79) = 0, (80) = 0, (81) = 0, (82) = 0, (83) = 0, (84) = 0, (85) = 0, (86) = 0, (87) = 0, (88) = 0, (89) = 0, (90) = 0, (91) = 0, (92) = 0, (93) = 0, (94) = 0, (95) = 0, (96) = 0, (97) = 0, (98) = 0, (99) = 0, (100) = 0})`

 

`Array(0..100, {(1) = 0, (2) = 0, (3) = 0, (4) = 0, (5) = 0, (6) = 0, (7) = 0, (8) = 0, (9) = 0, (10) = 0, (11) = 0, (12) = 0, (13) = 0, (14) = 0, (15) = 0, (16) = 0, (17) = 0, (18) = 0, (19) = 0, (20) = 0, (21) = 0, (22) = 0, (23) = 0, (24) = 0, (25) = 0, (26) = 0, (27) = 0, (28) = 0, (29) = 0, (30) = 0, (31) = 0, (32) = 0, (33) = 0, (34) = 0, (35) = 0, (36) = 0, (37) = 0, (38) = 0, (39) = 0, (40) = 0, (41) = 0, (42) = 0, (43) = 0, (44) = 0, (45) = 0, (46) = 0, (47) = 0, (48) = 0, (49) = 0, (50) = 0, (51) = 0, (52) = 0, (53) = 0, (54) = 0, (55) = 0, (56) = 0, (57) = 0, (58) = 0, (59) = 0, (60) = 0, (61) = 0, (62) = 0, (63) = 0, (64) = 0, (65) = 0, (66) = 0, (67) = 0, (68) = 0, (69) = 0, (70) = 0, (71) = 0, (72) = 0, (73) = 0, (74) = 0, (75) = 0, (76) = 0, (77) = 0, (78) = 0, (79) = 0, (80) = 0, (81) = 0, (82) = 0, (83) = 0, (84) = 0, (85) = 0, (86) = 0, (87) = 0, (88) = 0, (89) = 0, (90) = 0, (91) = 0, (92) = 0, (93) = 0, (94) = 0, (95) = 0, (96) = 0, (97) = 0, (98) = 0, (99) = 0, (100) = 0})`

 

`Array(0..100, {(1) = 0, (2) = 0, (3) = 0, (4) = 0, (5) = 0, (6) = 0, (7) = 0, (8) = 0, (9) = 0, (10) = 0, (11) = 0, (12) = 0, (13) = 0, (14) = 0, (15) = 0, (16) = 0, (17) = 0, (18) = 0, (19) = 0, (20) = 0, (21) = 0, (22) = 0, (23) = 0, (24) = 0, (25) = 0, (26) = 0, (27) = 0, (28) = 0, (29) = 0, (30) = 0, (31) = 0, (32) = 0, (33) = 0, (34) = 0, (35) = 0, (36) = 0, (37) = 0, (38) = 0, (39) = 0, (40) = 0, (41) = 0, (42) = 0, (43) = 0, (44) = 0, (45) = 0, (46) = 0, (47) = 0, (48) = 0, (49) = 0, (50) = 0, (51) = 0, (52) = 0, (53) = 0, (54) = 0, (55) = 0, (56) = 0, (57) = 0, (58) = 0, (59) = 0, (60) = 0, (61) = 0, (62) = 0, (63) = 0, (64) = 0, (65) = 0, (66) = 0, (67) = 0, (68) = 0, (69) = 0, (70) = 0, (71) = 0, (72) = 0, (73) = 0, (74) = 0, (75) = 0, (76) = 0, (77) = 0, (78) = 0, (79) = 0, (80) = 0, (81) = 0, (82) = 0, (83) = 0, (84) = 0, (85) = 0, (86) = 0, (87) = 0, (88) = 0, (89) = 0, (90) = 0, (91) = 0, (92) = 0, (93) = 0, (94) = 0, (95) = 0, (96) = 0, (97) = 0, (98) = 0, (99) = 0, (100) = 0})`

 

STEP 4: INITIAL CONDITION
  u(x,0) = A0·cos(πx/L)
  ∂u/∂t(x,0) = 0
  This creates the first harmonic vibration mode.
  The rod is maximally stretched at the middle (x = 0.02 m).

  Initial values for the first 5 points (i=0 to 4):
    u[0](0) = 2.40e-07 m at x = 0.0000 m
    u[1](0) = 2.40e-07 m at x = 0.0004 m
    u[2](0) = 2.40e-07 m at x = 0.0008 m
    u[3](0) = 2.39e-07 m at x = 0.0012 m
    u[4](0) = 2.38e-07 m at x = 0.0016 m
    ...

 

 

Warning, (in apply_bc) `u_new` is implicitly declared local

 

STEP 5: BOUNDARY CONDITIONS
  Neumann: ∂u/∂x = 0 at x = 0 and x = L
  This means: u(t) = u(t) and u_100(t) = u_99(t)
  Physically: The ends are FREE (can move without force).
  Waves reflect at the ends WITHOUT sign reversal.

STEP 6: THE COUPLED ODE SYSTEM
  After spatial discretization, we get 99 coupled ODEs:

  ODE 1: d²u_1/dt² = c²(λ(H + (u_2-u_0)/(2dx))) · (u_2-2u_1+u_0)/dx²
  ODE 2: d²u_2/dt² = c²(λ(H + (u_3-u_1)/(2dx))) · (u_3-2u_2+u_1)/dx²
  ODE 3: d²u_3/dt² = c²(λ(H + (u_4-u_2)/(2dx))) · (u_4-2u_3+u_2)/dx²
  ...
  ODE 97: d²u_97/dt² = c²(λ(H + (u_98-u_96)/(2dx))) · (u_98-2u_97+u_96)/dx²
  ODE 98: d²u_98/dt² = c²(λ(H + (u_99-u_97)/(2dx))) · (u_99-2u_98+u_97)/dx²
  ODE 99: d²u_99/dt² = c²(λ(H + (u_100-u_98)/(2dx))) · (u_100-2u_99+u_98)/dx²

  Each ODE connects point i with its neighbors i-1 and i+1.
  This is why they are called COUPLED ODEs.

STEP 7: SOLVING THE COUPLED ODEs
  Using explicit time integration (central differences):
    u_new[i] = 2*u_curr[i] - u_old[i] + dt² · (d²u_i/dt²)

  Starting numerical integration...

 

 

[42, 106, 211, 317, 422]

 

table( [ ] )

 

  Stored: t = 0.099 T0 (time step 42/1500)
  Stored: t = 0.251 T0 (time step 106/1500)
  Stored: t = 0.500 T0 (time step 211/1500)
  Stored: t = 0.751 T0 (time step 317/1500)
  Stored: t = 0.999 T0 (time step 422/1500)
  Progress: 500/1500 (33%)
  Progress: 1000/1500 (67%)
  Progress: 1500/1500 (100%)

Simulation completed!

STEP 8: VISUALIZATION
  Generating plots of the solution u_i(t)...

 

["red", "blue", "green", "magenta", "black"]

 

[]

 

 

  Plot shows the wave at 5 different times.

STEP 9: ANIMATION
  Generating animation of the moving wave...

 

[]

 

[.1, .25, .5, .75, 1.0]

 

["t = 0.10 Tâ‚€: Wave begins moving right.
The wave deforms because c² depends on strain.", "t = 0.25 T₀: Wave approaches the right end.
Amplitude changes due to nonlinearity.", "t = 0.50 Tâ‚€: Wave reaches the end and reflects.
&PartialD;u/&PartialD;x=0 at the end (free boundary).", "t = 0.75 Tâ‚€: Wave moves back left.
Wave shape is mirrored.", "t = 1.00 Tâ‚€: Wave returns to initial condition.
One complete period completed."]

 

[]

 

 

  Animation started! (20 frames)

========================================
              SUMMARY
========================================

WHAT IS A COUPLED ODE SYSTEM?
  • We have 99 unknown functions u_i(t), i = 1..99
  • Each ODE connects u_i with its neighbors u_{i-1} and u_{i+1}
  • This is why they are called COUPLED ODEs

WHERE ARE THE ODEs IN THE CODE?
  • Inside the main time loop (for n from 1 to Nt)
  • In the inner loop (for i from 1 to Nx-1)
  • Each iteration computes ONE ODE for point i

WHAT DOES EACH ODE DO?
  • Computes d²u_i/dt² = c² · (u_{i+1}-2u_i+u_{i-1})/dx²
  • Updates u_new[i] = 2*u_curr[i] - u_old[i] + dt² · (d²u_i/dt²)

ANSWER TO THE ORIGINAL QUESTION:
  Q: Can pdsolve handle c²(λ(H + ∂u/∂x))?
  A: NO, pdsolve CANNOT handle this nonlinear PDE.
  SOLUTION: Convert to COUPLED ODEs and use finite differences.
========================================

 

 


 

Download pde_vrgstuk_Can_pdsolve_handle_a_procedure_in_the_pde_mprimesDEFA_18-4-2026.mw

@Rouben Rostamian  
Thanks for the code! It showed me a different way to use the Euler-Lagrange differential equation. for his first derivative 

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