In [1]:
%reload_ext autoreload
%autoreload 2
In [2]:
from PDESolver import *
psiOp quantization and evolution simulation¶
Kohn–Nirenberg Quantization with FFT¶
1D influence of parameters: freq_windows, clamp_values and space_windows¶
Basic case¶
In [3]:
# --- Define symbols and dummy equation ---
x, xi = symbols('x xi', real=True)
u = Function('u')(x)
equation = Eq(psiOp(x**2 + xi**2 + 1, u), 0)
# --- Initialize the solver ---
solver = PDESolver(equation)
L = 10
N = 1024
solver.setup(Lx=L, Nx=N)
# --- Define test input and target output ---
x_grid = solver.x_grid
f_exact = np.exp(-x_grid**2)
g_exact = 3 * (1 - x_grid**2) * f_exact
# --- Define the symbol and its inverse ---
def S(x, xi):
return x**2 + xi**2 + 1
def S_inv(x, xi):
return 1.0 / (S(x, xi) + 1e-10)
# --- Parameter combinations to test ---
freq_windows = [None, 'gaussian', 'hann']
clamp_values = [1e2, 1e4, 1e6]
space_windows = [False, True]
combinations = list(product(freq_windows, clamp_values, space_windows))
# --- Store errors for analysis ---
results = []
# --- Loop over parameter configurations ---
for i, (fw, clamp, sw) in enumerate(combinations):
label = f"fw={fw}, clamp={clamp:.0e}, sw={sw}"
# --- Apply operator ψOp(S)[f] ---
g_approx = solver.kohn_nirenberg_fft(f_exact, symbol_func=S,
freq_window=fw,
clamp=clamp,
space_window=sw)
# --- Compute forward error ---
err_forward = np.linalg.norm(g_exact - g_approx) / np.linalg.norm(g_exact)
# --- Apply inverse ψOp(S⁻¹)[g] ---
f_approx = solver.kohn_nirenberg_fft(g_approx, symbol_func=S_inv,
freq_window=fw,
clamp=clamp,
space_window=sw)
# --- Compute inverse error ---
err_inverse = np.linalg.norm(f_exact - f_approx) / np.linalg.norm(f_exact)
# --- Save results ---
results.append((fw, clamp, sw, err_forward, err_inverse))
# --- Plot results for this configuration ---
plt.figure(figsize=(10, 4))
plt.subplot(1, 2, 1)
plt.plot(x_grid, g_exact, label='g_exact', lw=2)
plt.plot(x_grid, g_approx, '--', label='ψOp(S)[f]', lw=2)
plt.title(f"Forward: {label}\nerr={err_forward:.2e}")
plt.grid(True)
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(x_grid, f_exact, label='f_exact', lw=2)
plt.plot(x_grid, f_approx, '--', label='ψOp(S⁻¹)[g]', lw=2)
plt.title(f"Inverse: {label}\nerr={err_inverse:.2e}")
plt.grid(True)
plt.legend()
plt.suptitle("Kohn–Nirenberg Evaluation")
plt.tight_layout()
plt.show()
# --- Summary Table ---
print("\nSummary of Relative Errors:")
print(f"{'FreqWin':>10} {'Clamp':>10} {'SpaceWin':>10} {'ErrFwd':>12} {'ErrInv':>12}")
for fw, clamp, sw, err1, err2 in results:
print(f"{str(fw):>10} {clamp:10.0e} {str(sw):>10} {err1:12.2e} {err2:12.2e}")
*********************************
* Partial differential equation *
*********************************
⎛ 2 2 ⎞
psiOp⎝x + ξ + 1, u(x)⎠ = 0
********************
* Equation parsing *
********************
Equation rewritten in standard form: psiOp(x**2 + xi**2 + 1, u(x))
⚠️ psiOp detected: skipping expansion for safety
Expanded equation: psiOp(x**2 + xi**2 + 1, u(x))
Analyzing term: psiOp(x**2 + xi**2 + 1, u(x))
--> Classified as pseudo linear term (psiOp)
Final linear terms: {}
Final nonlinear terms: []
Symbol terms: []
Pseudo terms: [(1, x**2 + xi**2 + 1)]
Source terms: []
⚠️ Pseudo‑differential operator detected: all other linear terms have been rejected.
symbol =
2 2
x + ξ + 1
⚠️ For psiOp, use interactive_symbol_analysis.
/home/fifi/anaconda3/lib/python3.12/site-packages/matplotlib/cbook.py:1709: ComplexWarning: Casting complex values to real discards the imaginary part return math.isfinite(val) /home/fifi/anaconda3/lib/python3.12/site-packages/matplotlib/cbook.py:1345: ComplexWarning: Casting complex values to real discards the imaginary part return np.asarray(x, float)
Summary of Relative Errors:
FreqWin Clamp SpaceWin ErrFwd ErrInv
None 1e+02 False 6.97e-10 1.59e-01
None 1e+02 True 1.65e-02 1.60e-01
None 1e+04 False 6.98e-10 1.59e-01
None 1e+04 True 1.65e-02 1.60e-01
None 1e+06 False 1.46e-09 1.59e-01
None 1e+06 True 1.65e-02 1.60e-01
gaussian 1e+02 False 1.06e-08 1.59e-01
gaussian 1e+02 True 1.65e-02 1.60e-01
gaussian 1e+04 False 1.06e-08 1.59e-01
gaussian 1e+04 True 1.65e-02 1.60e-01
gaussian 1e+06 False 1.06e-08 1.59e-01
gaussian 1e+06 True 1.65e-02 1.60e-01
hann 1e+02 False 1.20e-04 1.59e-01
hann 1e+02 True 1.65e-02 1.60e-01
hann 1e+04 False 1.20e-04 1.59e-01
hann 1e+04 True 1.65e-02 1.60e-01
hann 1e+06 False 1.20e-04 1.59e-01
hann 1e+06 True 1.65e-02 1.60e-01
Advanced case¶
In [4]:
# --- Variables symboliques ---
x, xi = symbols('x xi', real=True)
u = Function('u')(x)
p_expr = (1 + 0.5 * sin(3 * x)) * xi**2
equation = Eq(psiOp(p_expr, u), 0)
# --- Initialisation du solveur 1D ---
solver1d = PDESolver(equation)
L = 10.0
N = 1024
solver1d.setup(Lx=L, Nx=N)
X = solver1d.X
KX = solver1d.KX
# --- Fonction d'entrée : onde sinusoïdale localisée ---
f_exact = np.exp(-X**2) * np.cos(10 * X)
# --- Symbole et inverse ---
def p_lens(x, xi):
return (1 + 0.5 * np.sin(3 * x)) * xi**2
def p_lens_inv(x, xi):
return 1.0 / (p_lens(x, xi) + 1e-6)
# --- Store errors and amplification ratio ---
results = []
# --- Parameter combinations to test ---
freq_windows = [None, 'gaussian', 'hann']
clamp_values = [1e2, 1e4, 1e6]
space_windows = [False, True]
combinations = list(product(freq_windows, clamp_values, space_windows))
# --- Loop over parameter configurations ---
for i, (fw, clamp, sw) in enumerate(combinations):
label = f"fw={fw}, clamp={clamp:.0e}, sw={sw}"
# --- Apply operator ψOp(S)[f] ---
g_approx = solver1d.kohn_nirenberg_fft(f_exact, symbol_func=p_lens,
freq_window=fw,
clamp=clamp,
space_window=sw)
# --- Compute amplification ---
ampl_ratio = np.linalg.norm(g_approx) / np.linalg.norm(f_exact)
# --- Apply inverse ψOp(S⁻¹)[g] ---
f_approx = solver1d.kohn_nirenberg_fft(g_approx, symbol_func=p_lens_inv,
freq_window=fw,
clamp=clamp,
space_window=sw)
# --- Compute inverse error ---
err_inverse = np.linalg.norm(f_exact - f_approx) / np.linalg.norm(f_exact)
# --- Save results ---
results.append((fw, clamp, sw, ampl_ratio, err_inverse))
# --- Plot results for this configuration ---
plt.figure(figsize=(10, 4))
plt.subplot(1, 2, 1)
plt.plot(X, g_approx.real, '--', label='ψOp(S)[f]', lw=2)
plt.title(f"Forward: {label}\n‖g‖/‖f‖ = {ampl_ratio:.2e}")
plt.grid(True)
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(X, f_exact, label='f_exact', lw=2)
plt.plot(X, f_approx.real, '--', label='ψOp(S⁻¹)[g]', lw=2)
plt.title(f"Inverse: {label}\nerr={err_inverse:.2e}")
plt.grid(True)
plt.legend()
plt.suptitle("Kohn–Nirenberg 1D Evaluation")
plt.tight_layout()
plt.show()
# --- Summary Table ---
print("\nSummary of Amplification and Inversion Errors:")
print(f"{'FreqWin':>10} {'Clamp':>10} {'SpaceWin':>10} {'‖g‖/‖f‖':>12} {'ErrInv':>12}")
for fw, clamp, sw, ampl, err2 in results:
print(f"{str(fw):>10} {clamp:10.0e} {str(sw):>10} {ampl:12.2e} {err2:12.2e}")
********************************* * Partial differential equation * *********************************
⎛ 2 ⎞
psiOp⎝ξ ⋅(0.5⋅sin(3⋅x) + 1), u(x)⎠ = 0
********************
* Equation parsing *
********************
Equation rewritten in standard form: psiOp(xi**2*(0.5*sin(3*x) + 1), u(x))
⚠️ psiOp detected: skipping expansion for safety
Expanded equation: psiOp(xi**2*(0.5*sin(3*x) + 1), u(x))
Analyzing term: psiOp(xi**2*(0.5*sin(3*x) + 1), u(x))
--> Classified as pseudo linear term (psiOp)
Final linear terms: {}
Final nonlinear terms: []
Symbol terms: []
Pseudo terms: [(1, xi**2*(0.5*sin(3*x) + 1))]
Source terms: []
⚠️ Pseudo‑differential operator detected: all other linear terms have been rejected.
symbol =
2
ξ ⋅(0.5⋅sin(3⋅x) + 1)
⚠️ For psiOp, use interactive_symbol_analysis.
Summary of Amplification and Inversion Errors:
FreqWin Clamp SpaceWin ‖g‖/‖f‖ ErrInv
None 1e+02 False 8.55e+01 2.32e+01
None 1e+02 True 8.46e+01 1.69e+01
None 1e+04 False 1.09e+02 3.34e-01
None 1e+04 True 1.08e+02 3.35e-01
None 1e+06 False 1.09e+02 3.34e-01
None 1e+06 True 1.08e+02 3.35e-01
gaussian 1e+02 False 8.55e+01 2.32e+01
gaussian 1e+02 True 8.46e+01 1.69e+01
gaussian 1e+04 False 1.09e+02 3.34e-01
gaussian 1e+04 True 1.08e+02 3.35e-01
gaussian 1e+06 False 1.09e+02 3.34e-01
gaussian 1e+06 True 1.08e+02 3.35e-01
hann 1e+02 False 8.53e+01 2.31e+01
hann 1e+02 True 8.44e+01 1.68e+01
hann 1e+04 False 1.09e+02 3.34e-01
hann 1e+04 True 1.08e+02 3.35e-01
hann 1e+06 False 1.09e+02 3.34e-01
hann 1e+06 True 1.08e+02 3.35e-01
2D influence of parameters: freq_windows, clamp_values and space_windows¶
Basic case¶
In [5]:
# --- Definition of symbolic variables ---
x, y, xi, eta = symbols('x y xi eta', real=True)
u = Function('u')(x, y)
# --- Definition of the symbol and associated equation ---
expr_symbol = x**2 + y**2 + xi**2 + eta**2 + 1
equation = Eq(psiOp(expr_symbol, u), 0) # Placeholder to initialize the solver
# --- Initialization of the 2D solver ---
solver = PDESolver(equation)
L = 10.0
N = 64 # Reduced to avoid MemoryError
solver.setup(Lx=L, Ly=L, Nx=N, Ny=N)
# --- Creation of the spatial grid ---
X, Y = solver.X, solver.Y
# --- Test function: Centered Gaussian ---
f_exact = np.exp(-(X**2 + Y**2))
# --- Target output: Op(p)[f] = (-3 x² - 3 y² + 5) * f_exact ---
g_exact = (-3 * X**2 - 3 * Y**2 + 5) * f_exact
# --- Definition of the symbol and its inverse ---
def S(x_val, y_val, xi_val, eta_val):
return x_val**2 + y_val**2 + xi_val**2 + eta_val**2 + 1
def S_inv(x_val, y_val, xi_val, eta_val):
return 1.0 / (S(x_val, y_val, xi_val, eta_val) + 1e-10)
# --- Parameters to test ---
freq_windows = [None, 'gaussian', 'hann']
clamp_values = [1e2, 1e4, 1e6]
space_windows = [False, True]
combinations = list(product(freq_windows, clamp_values, space_windows))
results = []
# --- Loop over parameter combinations ---
for i, (fw, clamp, sw) in enumerate(combinations):
label = f"fw={fw}, clamp={clamp:.0e}, sw={sw}"
# --- Application of the operator ψOp(S)[f] ---
g_approx = solver.kohn_nirenberg_fft(f_exact, symbol_func=S,
freq_window=fw,
clamp=clamp,
space_window=sw)
# --- Direct error ---
err_forward = np.linalg.norm(g_exact - g_approx) / np.linalg.norm(g_exact)
# --- Application of the inverse ψOp(S⁻¹)[g] ---
f_approx = solver.kohn_nirenberg_fft(g_approx, symbol_func=S_inv,
freq_window=fw,
clamp=clamp,
space_window=sw)
# --- Inverse error ---
err_inverse = np.linalg.norm(f_exact - f_approx) / np.linalg.norm(f_exact)
results.append((fw, clamp, sw, err_forward, err_inverse))
# --- Visualization ---
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
extent = [-L/2, L/2, -L/2, L/2]
im = axes[0, 0].imshow(f_exact, extent=extent, origin='lower', cmap='viridis')
plt.colorbar(im, ax=axes[0, 0])
axes[0, 0].set_title("Exact Input f(x,y)")
im = axes[0, 1].imshow(g_exact, extent=extent, origin='lower', cmap='viridis')
plt.colorbar(im, ax=axes[0, 1])
axes[0, 1].set_title(f"Exact Output g(x,y)\n{label}")
im = axes[1, 0].imshow(g_approx.real, extent=extent, origin='lower', cmap='viridis')
plt.colorbar(im, ax=axes[1, 0])
axes[1, 0].set_title(f"Approx Output ψOp(S)[f]\nerr={err_forward:.2e}")
im = axes[1, 1].imshow(f_approx.real, extent=extent, origin='lower', cmap='viridis')
plt.colorbar(im, ax=axes[1, 1])
axes[1, 1].set_title(f"Reconstructed f ≈ ψOp(S⁻¹)[g]\nerr={err_inverse:.2e}")
plt.suptitle("2D Kohn–Nirenberg Evaluation")
plt.tight_layout()
plt.show()
# --- Summary table of errors ---
print("\nSummary of Relative Errors:")
print(f"{'FreqWin':>10} {'Clamp':>10} {'SpaceWin':>10} {'ErrFwd':>12} {'ErrInv':>12}")
for fw, clamp, sw, err1, err2 in results:
print(f"{str(fw):>10} {clamp:10.0e} {str(sw):>10} {err1:12.2e} {err2:12.2e}")
*********************************
* Partial differential equation *
*********************************
⎛ 2 2 2 2 ⎞
psiOp⎝η + x + ξ + y + 1, u(x, y)⎠ = 0
********************
* Equation parsing *
********************
Equation rewritten in standard form: psiOp(eta**2 + x**2 + xi**2 + y**2 + 1, u(x, y))
⚠️ psiOp detected: skipping expansion for safety
Expanded equation: psiOp(eta**2 + x**2 + xi**2 + y**2 + 1, u(x, y))
Analyzing term: psiOp(eta**2 + x**2 + xi**2 + y**2 + 1, u(x, y))
--> Classified as pseudo linear term (psiOp)
Final linear terms: {}
Final nonlinear terms: []
Symbol terms: []
Pseudo terms: [(1, eta**2 + x**2 + xi**2 + y**2 + 1)]
Source terms: []
⚠️ Pseudo‑differential operator detected: all other linear terms have been rejected.
symbol =
2 2 2 2
η + x + ξ + y + 1
⚠️ For psiOp, use interactive_symbol_analysis.
Summary of Relative Errors:
FreqWin Clamp SpaceWin ErrFwd ErrInv
None 1e+02 False 8.52e-08 1.41e-01
None 1e+02 True 2.00e-02 1.42e-01
None 1e+04 False 3.77e-10 1.41e-01
None 1e+04 True 2.00e-02 1.42e-01
None 1e+06 False 3.77e-10 1.41e-01
None 1e+06 True 2.00e-02 1.42e-01
gaussian 1e+02 False 7.32e-04 1.41e-01
gaussian 1e+02 True 1.99e-02 1.41e-01
gaussian 1e+04 False 7.32e-04 1.41e-01
gaussian 1e+04 True 1.99e-02 1.41e-01
gaussian 1e+06 False 7.32e-04 1.41e-01
gaussian 1e+06 True 1.99e-02 1.41e-01
hann 1e+02 False 3.63e-02 1.14e-01
hann 1e+02 True 4.53e-02 1.20e-01
hann 1e+04 False 3.63e-02 1.14e-01
hann 1e+04 True 4.53e-02 1.20e-01
hann 1e+06 False 3.63e-02 1.14e-01
hann 1e+06 True 4.53e-02 1.20e-01
Advanced case¶
In [6]:
# --- Symbolic setup ---
x, y, xi, eta = symbols('x y xi eta', real=True)
u = Function('u')(x, y)
# --- Equation placeholder (symbol will be passed numerically) ---
symbol_expr = (1 + 0.5 * cos(3*x) * sin(2*y)) * (xi**2 + eta**2)
equation = Eq(psiOp(symbol_expr, u), 0)
solver = PDESolver(equation)
# --- Grid parameters ---
L = 10.0
N = 64
solver.setup(Lx=L, Ly=L, Nx=N, Ny=N)
X, Y = solver.X, solver.Y
KX, KY = solver.KX, solver.KY
# --- Test function: oscillating Gaussian (like a modulated laser beam) ---
f_exact = np.exp(-(X**2 + Y**2)) * np.cos(10 * X)
# --- Symbol definition ---
def p_quantum_lens(x, y, xi, eta):
mod = 1.0 + 0.5 * np.cos(3 * x) * np.sin(2 * y)
return mod * (xi**2 + eta**2)
def p_quantum_lens_inv(x, y, xi, eta):
return 1.0 / (p_quantum_lens(x, y, xi, eta) + 1e-6)
# --- Parameters to explore ---
freq_windows = [None, 'gaussian', 'hann']
space_windows = [False, True]
clamp = 1e4 # Fixed clamp to avoid instability
combinations = list(product(freq_windows, space_windows))
# --- Storage for summary ---
results = []
# --- Main loop ---
for fw, sw in combinations:
label = f"fw={fw}, sw={sw}"
g_approx = solver.kohn_nirenberg_fft(f_exact,
symbol_func=p_quantum_lens,
freq_window=fw,
space_window=sw,
clamp=clamp)
# --- Compute centered spectra ---
F_spec = np.log1p(np.abs(fftshift(fft2(f_exact))))
G_spec = np.log1p(np.abs(fftshift(fft2(g_approx))))
# --- Frequency axes for plotting (optional but helpful) ---
freq_extent = [-N/2, N/2, -N/2, N/2]
# --- Plotting ---
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
axes[0].imshow(F_spec, extent=freq_extent, origin='lower', cmap='plasma')
axes[0].set_title("FFT of f_exact")
axes[0].set_xlabel("ξ")
axes[0].set_ylabel("η")
axes[1].imshow(G_spec, extent=freq_extent, origin='lower', cmap='plasma')
axes[1].set_title("FFT of g_approx = ψOp(p)[f]")
axes[1].set_xlabel("ξ")
axes[1].set_ylabel("η")
plt.suptitle("Frequency content: input vs amplified output")
plt.tight_layout()
plt.show()
f_recon = solver.kohn_nirenberg_fft(g_approx,
symbol_func=p_quantum_lens_inv,
freq_window=fw,
space_window=sw,
clamp=clamp)
err_forward = np.linalg.norm(g_approx) / np.linalg.norm(f_exact)
err_inverse = np.linalg.norm(f_exact - f_recon) / np.linalg.norm(f_exact)
results.append((fw, sw, err_forward, err_inverse))
# --- Visualization ---
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
extent = [-L/2, L/2, -L/2, L/2]
im = axes[0, 0].imshow(f_exact, extent=extent, origin='lower', cmap='viridis')
plt.colorbar(im, ax=axes[0, 0])
axes[0, 0].set_title("Input: oscillating Gaussian")
im = axes[0, 1].imshow(g_approx.real, extent=extent, origin='lower', cmap='magma')
plt.colorbar(im, ax=axes[0, 1])
axes[0, 1].set_title(f"ψOp(p)[f] {label}\n(norm={err_forward:.2e})")
im = axes[1, 0].imshow(f_recon.real, extent=extent, origin='lower', cmap='viridis')
plt.colorbar(im, ax=axes[1, 0])
axes[1, 0].set_title(f"Reconstructed f ≈ ψOp(p⁻¹)[g]")
im = axes[1, 1].imshow((f_recon - f_exact).real, extent=extent, origin='lower', cmap='inferno')
plt.colorbar(im, ax=axes[1, 1])
axes[1, 1].set_title(f"Error f - f_recon\n(err={err_inverse:.2e})")
plt.suptitle(f"Kohn–Nirenberg Diffraction Test\n{label}")
plt.tight_layout()
plt.show()
# --- Summary table of errors ---
print("\nSummary of Relative Errors:")
print(f"{'FreqWin':>10} {'SpaceWin':>10} {'ErrFwd':>12} {'ErrInv':>12}")
for fw, sw, err1, err2 in results:
print(f"{str(fw):>10} {str(sw):>10} {err1:12.2e} {err2:12.2e}")
*********************************
* Partial differential equation *
*********************************
⎛⎛ 2 2⎞ ⎞
psiOp⎝⎝η + ξ ⎠⋅(0.5⋅sin(2⋅y)⋅cos(3⋅x) + 1), u(x, y)⎠ = 0
********************
* Equation parsing *
********************
Equation rewritten in standard form: psiOp((eta**2 + xi**2)*(0.5*sin(2*y)*cos(3*x) + 1), u(x, y))
⚠️ psiOp detected: skipping expansion for safety
Expanded equation: psiOp((eta**2 + xi**2)*(0.5*sin(2*y)*cos(3*x) + 1), u(x, y))
Analyzing term: psiOp((eta**2 + xi**2)*(0.5*sin(2*y)*cos(3*x) + 1), u(x, y))
--> Classified as pseudo linear term (psiOp)
Final linear terms: {}
Final nonlinear terms: []
Symbol terms: []
Pseudo terms: [(1, (eta**2 + xi**2)*(0.5*sin(2*y)*cos(3*x) + 1))]
Source terms: []
⚠️ Pseudo‑differential operator detected: all other linear terms have been rejected.
symbol =
⎛ 2 2⎞
⎝η + ξ ⎠⋅(0.5⋅sin(2⋅y)⋅cos(3⋅x) + 1)
⚠️ For psiOp, use interactive_symbol_analysis.
Summary of Relative Errors:
FreqWin SpaceWin ErrFwd ErrInv
None False 1.07e+02 1.77e-01
None True 1.04e+02 1.80e-01
gaussian False 8.93e+01 3.35e-01
gaussian True 8.73e+01 3.56e-01
hann False 5.10e+01 7.59e-01
hann True 4.98e+01 7.68e-01
Non-periodic Kohn–Nirenberg Quantization¶
1D¶
Basic case¶
In [7]:
# --- Define symbols and dummy equation ---
x, xi = symbols('x xi', real=True)
u = Function('u')(x)
equation = Eq(psiOp(x**2 + xi**2 + 1, u), 0)
# --- Initialize the solver ---
solver = PDESolver(equation)
L = 10
N = 1024
solver.setup(Lx=L, Nx=N)
# --- Define test input and target output ---
x_grid = solver.x_grid
xi_grid = np.linspace(-20, 20, 1024)
f_exact = np.exp(-x_grid**2)
g_exact = 3 * (1 - x_grid**2) * f_exact
# --- Define the symbol and its inverse ---
def S(x, xi):
return x**2 + xi**2 + 1
def S_inv(x, xi):
return 1.0 / (S(x, xi) + 1e-10)
# --- Parameter combinations to test ---
freq_windows = [None, 'gaussian', 'hann']
clamp_values = [1e2, 1e4, 1e6]
space_windows = [False, True]
combinations = list(product(freq_windows, clamp_values, space_windows))
# --- Store errors for analysis ---
results = []
# --- Loop over parameter configurations ---
for i, (fw, clamp, sw) in enumerate(combinations):
label = f"fw={fw}, clamp={clamp:.0e}, sw={sw}"
# --- Apply operator ψOp(S)[f] ---
g_approx = solver.kohn_nirenberg_nonperiodic(f_exact, x_grid, xi_grid, S,
freq_window=fw,
clamp=clamp,
space_window=sw)
# --- Compute amplification ---
ampl_ratio = np.linalg.norm(g_approx) / np.linalg.norm(f_exact)
# --- Apply inverse ψOp(S⁻¹)[g] ---
f_approx = solver.kohn_nirenberg_nonperiodic(g_approx, x_grid, xi_grid, S_inv,
freq_window=fw,
clamp=clamp,
space_window=sw)
# --- Compute inverse error ---
err_inverse = np.linalg.norm(f_exact - f_approx) / np.linalg.norm(f_exact)
# --- Save results ---
results.append((fw, clamp, sw, err_forward, err_inverse))
# --- Plot results for this configuration ---
plt.figure(figsize=(10, 4))
plt.subplot(1, 2, 1)
plt.plot(x_grid, g_exact, label='g_exact', lw=2)
plt.plot(x_grid, g_approx, '--', label='ψOp(S)[f]', lw=2)
plt.title(f"Forward: {label}\nerr={err_forward:.2e}")
plt.grid(True)
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(x_grid, f_exact, label='f_exact', lw=2)
plt.plot(x_grid, f_approx, '--', label='ψOp(S⁻¹)[g]', lw=2)
plt.title(f"Inverse: {label}\nerr={err_inverse:.2e}")
plt.grid(True)
plt.legend()
plt.suptitle("Kohn–Nirenberg Evaluation")
plt.tight_layout()
plt.show()
# --- Summary Table ---
print("\nSummary of Relative Errors:")
print(f"{'FreqWin':>10} {'Clamp':>10} {'SpaceWin':>10} {'ErrFwd':>12} {'ErrInv':>12}")
for fw, clamp, sw, err1, err2 in results:
print(f"{str(fw):>10} {clamp:10.0e} {str(sw):>10} {err1:12.2e} {err2:12.2e}")
*********************************
* Partial differential equation *
*********************************
⎛ 2 2 ⎞
psiOp⎝x + ξ + 1, u(x)⎠ = 0
********************
* Equation parsing *
********************
Equation rewritten in standard form: psiOp(x**2 + xi**2 + 1, u(x))
⚠️ psiOp detected: skipping expansion for safety
Expanded equation: psiOp(x**2 + xi**2 + 1, u(x))
Analyzing term: psiOp(x**2 + xi**2 + 1, u(x))
--> Classified as pseudo linear term (psiOp)
Final linear terms: {}
Final nonlinear terms: []
Symbol terms: []
Pseudo terms: [(1, x**2 + xi**2 + 1)]
Source terms: []
⚠️ Pseudo‑differential operator detected: all other linear terms have been rejected.
symbol =
2 2
x + ξ + 1
⚠️ For psiOp, use interactive_symbol_analysis.
Summary of Relative Errors:
FreqWin Clamp SpaceWin ErrFwd ErrInv
None 1e+02 False 4.98e+01 1.59e-01
None 1e+02 True 4.98e+01 1.60e-01
None 1e+04 False 4.98e+01 1.59e-01
None 1e+04 True 4.98e+01 1.60e-01
None 1e+06 False 4.98e+01 1.59e-01
None 1e+06 True 4.98e+01 1.60e-01
gaussian 1e+02 False 4.98e+01 1.59e-01
gaussian 1e+02 True 4.98e+01 1.59e-01
gaussian 1e+04 False 4.98e+01 1.59e-01
gaussian 1e+04 True 4.98e+01 1.59e-01
gaussian 1e+06 False 4.98e+01 1.59e-01
gaussian 1e+06 True 4.98e+01 1.59e-01
hann 1e+02 False 4.98e+01 1.42e-01
hann 1e+02 True 4.98e+01 1.43e-01
hann 1e+04 False 4.98e+01 1.42e-01
hann 1e+04 True 4.98e+01 1.43e-01
hann 1e+06 False 4.98e+01 1.42e-01
hann 1e+06 True 4.98e+01 1.43e-01
Advanced case¶
In [8]:
# --- Variables symboliques ---
x, xi = symbols('x xi', real=True)
u = Function('u')(x)
p_expr = (1 + 0.5 * sin(3 * x)) * xi**2
equation = Eq(psiOp(p_expr, u), 0)
# --- Initialisation du solveur 1D ---
solver1d = PDESolver(equation)
L = 10.0
N = 1024
solver1d.setup(Lx=L, Nx=N)
x_grid = solver.x_grid
xi_grid = np.linspace(-20, 20, 1024)
X = solver1d.X
KX = solver1d.KX
# --- Fonction d'entrée : onde sinusoïdale localisée ---
f_exact = np.exp(-X**2) * np.cos(10 * X)
# --- Symbole et inverse ---
def p_lens(x, xi):
return (1 + 0.5 * np.sin(3 * x)) * xi**2
def p_lens_inv(x, xi):
return 1.0 / (p_lens(x, xi) + 1e-6)
# --- Store errors and amplification ratio ---
results = []
# --- Parameter combinations to test ---
freq_windows = [None, 'gaussian', 'hann']
clamp_values = [1e2, 1e4, 1e6]
space_windows = [False, True]
combinations = list(product(freq_windows, clamp_values, space_windows))
# --- Loop over parameter configurations ---
for i, (fw, clamp, sw) in enumerate(combinations):
label = f"fw={fw}, clamp={clamp:.0e}, sw={sw}"
# --- Apply operator ψOp(S)[f] ---
g_approx = solver.kohn_nirenberg_nonperiodic(f_exact, x_grid, xi_grid, S,
freq_window=fw,
clamp=clamp,
space_window=sw)
# --- Compute forward error ---
err_forward = np.linalg.norm(g_exact - g_approx) / np.linalg.norm(g_exact)
# --- Apply inverse ψOp(S⁻¹)[g] ---
f_approx = solver.kohn_nirenberg_nonperiodic(g_approx, x_grid, xi_grid, S_inv,
freq_window=fw,
clamp=clamp,
space_window=sw)
# --- Compute inverse error ---
err_inverse = np.linalg.norm(f_exact - f_approx) / np.linalg.norm(f_exact)
# --- Save results ---
results.append((fw, clamp, sw, ampl_ratio, err_inverse))
# --- Plot results for this configuration ---
plt.figure(figsize=(10, 4))
plt.subplot(1, 2, 1)
plt.plot(X, g_approx.real, '--', label='ψOp(S)[f]', lw=2)
plt.title(f"Forward: {label}\n‖g‖/‖f‖ = {ampl_ratio:.2e}")
plt.grid(True)
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(X, f_exact, label='f_exact', lw=2)
plt.plot(X, f_approx.real, '--', label='ψOp(S⁻¹)[g]', lw=2)
plt.title(f"Inverse: {label}\nerr={err_inverse:.2e}")
plt.grid(True)
plt.legend()
plt.suptitle("Kohn–Nirenberg 1D Evaluation")
plt.tight_layout()
plt.show()
# --- Summary Table ---
print("\nSummary of Amplification and Inversion Errors:")
print(f"{'FreqWin':>10} {'Clamp':>10} {'SpaceWin':>10} {'‖g‖/‖f‖':>12} {'ErrInv':>12}")
for fw, clamp, sw, ampl, err2 in results:
print(f"{str(fw):>10} {clamp:10.0e} {str(sw):>10} {ampl:12.2e} {err2:12.2e}")
*********************************
* Partial differential equation *
*********************************
⎛ 2 ⎞
psiOp⎝ξ ⋅(0.5⋅sin(3⋅x) + 1), u(x)⎠ = 0
********************
* Equation parsing *
********************
Equation rewritten in standard form: psiOp(xi**2*(0.5*sin(3*x) + 1), u(x))
⚠️ psiOp detected: skipping expansion for safety
Expanded equation: psiOp(xi**2*(0.5*sin(3*x) + 1), u(x))
Analyzing term: psiOp(xi**2*(0.5*sin(3*x) + 1), u(x))
--> Classified as pseudo linear term (psiOp)
Final linear terms: {}
Final nonlinear terms: []
Symbol terms: []
Pseudo terms: [(1, xi**2*(0.5*sin(3*x) + 1))]
Source terms: []
⚠️ Pseudo‑differential operator detected: all other linear terms have been rejected.
symbol =
2
ξ ⋅(0.5⋅sin(3⋅x) + 1)
⚠️ For psiOp, use interactive_symbol_analysis.
Summary of Amplification and Inversion Errors:
FreqWin Clamp SpaceWin ‖g‖/‖f‖ ErrInv
None 1e+02 False 2.42e+00 1.20e-01
None 1e+02 True 2.42e+00 1.26e-01
None 1e+04 False 2.42e+00 2.27e-03
None 1e+04 True 2.42e+00 3.30e-02
None 1e+06 False 2.42e+00 2.27e-03
None 1e+06 True 2.42e+00 3.30e-02
gaussian 1e+02 False 2.42e+00 3.46e-01
gaussian 1e+02 True 2.42e+00 3.56e-01
gaussian 1e+04 False 2.42e+00 2.84e-01
gaussian 1e+04 True 2.42e+00 2.98e-01
gaussian 1e+06 False 2.42e+00 2.84e-01
gaussian 1e+06 True 2.42e+00 2.98e-01
hann 1e+02 False 2.42e+00 7.61e-01
hann 1e+02 True 2.42e+00 7.66e-01
hann 1e+04 False 2.42e+00 7.48e-01
hann 1e+04 True 2.42e+00 7.52e-01
hann 1e+06 False 2.42e+00 7.48e-01
hann 1e+06 True 2.42e+00 7.52e-01
2D¶
Basic case¶
In [9]:
# --- Definition of symbolic variables ---
x, y, xi, eta = symbols('x y xi eta', real=True)
u = Function('u')(x, y)
# --- Definition of the symbol and associated equation ---
expr_symbol = x**2 + y**2 + xi**2 + eta**2 + 1
equation = Eq(psiOp(expr_symbol, u), 0) # Placeholder to initialize the solver
# --- Initialization of the 2D solver ---
solver = PDESolver(equation)
L = 10.0
N = 64 # Reduced to avoid MemoryError
solver.setup(Lx=L, Ly=L, Nx=N, Ny=N)
# --- Creation of the spatial grid ---
X, Y = solver.X, solver.Y
# Spatial grids
x_grid = solver.x_grid
y_grid = solver.y_grid
# Non-periodic frequency grids
xi_grid = np.linspace(-15, 15, N)
eta_grid = np.linspace(-15, 15, N)
# --- Test function: Centered Gaussian ---
f_exact = np.exp(-(X**2 + Y**2))
# --- Target output: Op(p)[f] = (-3 x² - 3 y² + 5) * f_exact ---
g_exact = (-3 * X**2 - 3 * Y**2 + 5) * f_exact
# --- Definition of the symbol and its inverse ---
def S(x_val, y_val, xi_val, eta_val):
return x_val**2 + y_val**2 + xi_val**2 + eta_val**2 + 1
def S_inv(x_val, y_val, xi_val, eta_val):
return 1.0 / (S(x_val, y_val, xi_val, eta_val) + 1e-10)
# --- Parameters to test ---
freq_windows = [None, 'gaussian', 'hann']
clamp_values = [1e2, 1e4, 1e6]
space_windows = [False, True]
combinations = list(product(freq_windows, clamp_values, space_windows))
results = []
# --- Loop over parameter combinations ---
for i, (fw, clamp, sw) in enumerate(combinations):
label = f"fw={fw}, clamp={clamp:.0e}, sw={sw}"
# --- Application of the operator ψOp(S)[f] ---
g_approx = solver.kohn_nirenberg_nonperiodic(f_exact,
x_grid=(x_grid, y_grid),
xi_grid=(xi_grid, eta_grid),
symbol_func=S,
freq_window=fw,
clamp=clamp,
space_window=sw)
# --- Direct error ---
err_forward = np.linalg.norm(g_exact - g_approx) / np.linalg.norm(g_exact)
# --- Application of the inverse ψOp(S⁻¹)[g] ---
f_approx = solver.kohn_nirenberg_nonperiodic(g_approx,
x_grid=(x_grid, y_grid),
xi_grid=(xi_grid, eta_grid),
symbol_func=S_inv,
freq_window=fw,
clamp=clamp,
space_window=sw)
# --- Inverse error ---
err_inverse = np.linalg.norm(f_exact - f_approx) / np.linalg.norm(f_exact)
results.append((fw, clamp, sw, err_forward, err_inverse))
# --- Visualization ---
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
extent = [-L/2, L/2, -L/2, L/2]
im = axes[0, 0].imshow(f_exact, extent=extent, origin='lower', cmap='viridis')
plt.colorbar(im, ax=axes[0, 0])
axes[0, 0].set_title("Exact Input f(x,y)")
im = axes[0, 1].imshow(g_exact, extent=extent, origin='lower', cmap='viridis')
plt.colorbar(im, ax=axes[0, 1])
axes[0, 1].set_title(f"Exact Output g(x,y)\n{label}")
im = axes[1, 0].imshow(g_approx.real, extent=extent, origin='lower', cmap='viridis')
plt.colorbar(im, ax=axes[1, 0])
axes[1, 0].set_title(f"Approx Output ψOp(S)[f]\nerr={err_forward:.2e}")
im = axes[1, 1].imshow(f_approx.real, extent=extent, origin='lower', cmap='viridis')
plt.colorbar(im, ax=axes[1, 1])
axes[1, 1].set_title(f"Reconstructed f ≈ ψOp(S⁻¹)[g]\nerr={err_inverse:.2e}")
plt.suptitle("2D Kohn–Nirenberg Evaluation")
plt.tight_layout()
plt.show()
# --- Summary table of errors ---
print("\nSummary of Relative Errors:")
print(f"{'FreqWin':>10} {'Clamp':>10} {'SpaceWin':>10} {'ErrFwd':>12} {'ErrInv':>12}")
for fw, clamp, sw, err1, err2 in results:
print(f"{str(fw):>10} {clamp:10.0e} {str(sw):>10} {err1:12.2e} {err2:12.2e}")
*********************************
* Partial differential equation *
*********************************
⎛ 2 2 2 2 ⎞
psiOp⎝η + x + ξ + y + 1, u(x, y)⎠ = 0
********************
* Equation parsing *
********************
Equation rewritten in standard form: psiOp(eta**2 + x**2 + xi**2 + y**2 + 1, u(x, y))
⚠️ psiOp detected: skipping expansion for safety
Expanded equation: psiOp(eta**2 + x**2 + xi**2 + y**2 + 1, u(x, y))
Analyzing term: psiOp(eta**2 + x**2 + xi**2 + y**2 + 1, u(x, y))
--> Classified as pseudo linear term (psiOp)
Final linear terms: {}
Final nonlinear terms: []
Symbol terms: []
Pseudo terms: [(1, eta**2 + x**2 + xi**2 + y**2 + 1)]
Source terms: []
⚠️ Pseudo‑differential operator detected: all other linear terms have been rejected.
symbol =
2 2 2 2
η + x + ξ + y + 1
⚠️ For psiOp, use interactive_symbol_analysis.
Summary of Relative Errors:
FreqWin Clamp SpaceWin ErrFwd ErrInv
None 1e+02 False 4.38e-08 1.41e-01
None 1e+02 True 2.00e-02 1.42e-01
None 1e+04 False 9.20e-11 1.41e-01
None 1e+04 True 2.00e-02 1.42e-01
None 1e+06 False 9.20e-11 1.41e-01
None 1e+06 True 2.00e-02 1.42e-01
gaussian 1e+02 False 2.36e-03 1.40e-01
gaussian 1e+02 True 1.98e-02 1.41e-01
gaussian 1e+04 False 2.36e-03 1.40e-01
gaussian 1e+04 True 1.98e-02 1.41e-01
gaussian 1e+06 False 2.36e-03 1.40e-01
gaussian 1e+06 True 1.98e-02 1.41e-01
hann 1e+02 False 4.77e-02 1.56e-01
hann 1e+02 True 6.42e-02 1.71e-01
hann 1e+04 False 4.77e-02 1.56e-01
hann 1e+04 True 6.42e-02 1.71e-01
hann 1e+06 False 4.77e-02 1.56e-01
hann 1e+06 True 6.42e-02 1.71e-01
Advanced case¶
In [10]:
# --- Symbolic setup ---
x, y, xi, eta = symbols('x y xi eta', real=True)
u = Function('u')(x, y)
# --- Equation placeholder (symbol will be passed numerically) ---
symbol_expr = (1 + 0.5 * cos(3*x) * sin(2*y)) * (xi**2 + eta**2)
equation = Eq(psiOp(symbol_expr, u), 0)
solver = PDESolver(equation)
# --- Grid parameters ---
L = 10.0
N = 64
solver.setup(Lx=L, Ly=L, Nx=N, Ny=N)
X, Y = solver.X, solver.Y
KX, KY = solver.KX, solver.KY
# Spatial grids
x_grid = solver.x_grid
y_grid = solver.y_grid
# Non-periodic frequency grids
xi_grid = np.linspace(-15, 15, 64)
eta_grid = np.linspace(-15, 15, 64)
# --- Test function: oscillating Gaussian (like a modulated laser beam) ---
f_exact = np.exp(-(X**2 + Y**2)) * np.cos(10 * X)
# --- Symbol definition ---
def p_quantum_lens(x, y, xi, eta):
mod = 1.0 + 0.5 * np.cos(3 * x) * np.sin(2 * y)
return mod * (xi**2 + eta**2)
def p_quantum_lens_inv(x, y, xi, eta):
return 1.0 / (p_quantum_lens(x, y, xi, eta) + 1e-6)
# --- Parameters to explore ---
freq_windows = [None, 'gaussian', 'hann']
space_windows = [False, True]
clamp = 1e4 # Fixed clamp to avoid instability
combinations = list(product(freq_windows, space_windows))
# --- Storage for summary ---
results = []
# --- Main loop ---
for fw, sw in combinations:
label = f"fw={fw}, sw={sw}"
g_approx = solver.kohn_nirenberg_nonperiodic(f_exact,
x_grid=(x_grid, y_grid),
xi_grid=(xi_grid, eta_grid),
symbol_func=p_quantum_lens,
freq_window=fw,
clamp=clamp,
space_window=sw)
# --- Compute centered spectra ---
F_spec = np.log1p(np.abs(fftshift(fft2(f_exact))))
G_spec = np.log1p(np.abs(fftshift(fft2(g_approx))))
# --- Frequency axes for plotting (optional but helpful) ---
freq_extent = [-N/2, N/2, -N/2, N/2]
# --- Plotting ---
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
axes[0].imshow(F_spec, extent=freq_extent, origin='lower', cmap='plasma')
axes[0].set_title("FFT of f_exact")
axes[0].set_xlabel("ξ")
axes[0].set_ylabel("η")
axes[1].imshow(G_spec, extent=freq_extent, origin='lower', cmap='plasma')
axes[1].set_title("FFT of g_approx = ψOp(p)[f]")
axes[1].set_xlabel("ξ")
axes[1].set_ylabel("η")
plt.suptitle("Frequency content: input vs amplified output")
plt.tight_layout()
plt.show()
f_recon = solver.kohn_nirenberg_nonperiodic(g_approx,
x_grid=(x_grid, y_grid),
xi_grid=(xi_grid, eta_grid),
symbol_func=p_quantum_lens_inv,
freq_window=fw,
clamp=clamp,
space_window=sw)
err_forward = np.linalg.norm(g_approx) / np.linalg.norm(f_exact)
err_inverse = np.linalg.norm(f_exact - f_recon) / np.linalg.norm(f_exact)
results.append((fw, sw, err_forward, err_inverse))
# --- Visualization ---
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
extent = [-L/2, L/2, -L/2, L/2]
im = axes[0, 0].imshow(f_exact, extent=extent, origin='lower', cmap='viridis')
plt.colorbar(im, ax=axes[0, 0])
axes[0, 0].set_title("Input: oscillating Gaussian")
im = axes[0, 1].imshow(g_approx.real, extent=extent, origin='lower', cmap='magma')
plt.colorbar(im, ax=axes[0, 1])
axes[0, 1].set_title(f"ψOp(p)[f] {label}\n(norm={err_forward:.2e})")
im = axes[1, 0].imshow(f_recon.real, extent=extent, origin='lower', cmap='viridis')
plt.colorbar(im, ax=axes[1, 0])
axes[1, 0].set_title(f"Reconstructed f ≈ ψOp(p⁻¹)[g]")
im = axes[1, 1].imshow((f_recon - f_exact).real, extent=extent, origin='lower', cmap='inferno')
plt.colorbar(im, ax=axes[1, 1])
axes[1, 1].set_title(f"Error f - f_recon\n(err={err_inverse:.2e})")
plt.suptitle(f"Kohn–Nirenberg Diffraction Test\n{label}")
plt.tight_layout()
plt.show()
# --- Summary table of errors ---
print("\nSummary of Relative Errors:")
print(f"{'FreqWin':>10} {'SpaceWin':>10} {'ErrFwd':>12} {'ErrInv':>12}")
for fw, sw, err1, err2 in results:
print(f"{str(fw):>10} {str(sw):>10} {err1:12.2e} {err2:12.2e}")
*********************************
* Partial differential equation *
*********************************
⎛⎛ 2 2⎞ ⎞
psiOp⎝⎝η + ξ ⎠⋅(0.5⋅sin(2⋅y)⋅cos(3⋅x) + 1), u(x, y)⎠ = 0
********************
* Equation parsing *
********************
Equation rewritten in standard form: psiOp((eta**2 + xi**2)*(0.5*sin(2*y)*cos(3*x) + 1), u(x, y))
⚠️ psiOp detected: skipping expansion for safety
Expanded equation: psiOp((eta**2 + xi**2)*(0.5*sin(2*y)*cos(3*x) + 1), u(x, y))
Analyzing term: psiOp((eta**2 + xi**2)*(0.5*sin(2*y)*cos(3*x) + 1), u(x, y))
--> Classified as pseudo linear term (psiOp)
Final linear terms: {}
Final nonlinear terms: []
Symbol terms: []
Pseudo terms: [(1, (eta**2 + xi**2)*(0.5*sin(2*y)*cos(3*x) + 1))]
Source terms: []
⚠️ Pseudo‑differential operator detected: all other linear terms have been rejected.
symbol =
⎛ 2 2⎞
⎝η + ξ ⎠⋅(0.5⋅sin(2⋅y)⋅cos(3⋅x) + 1)
⚠️ For psiOp, use interactive_symbol_analysis.
Summary of Relative Errors:
FreqWin SpaceWin ErrFwd ErrInv
None False 1.07e+02 1.78e-01
None True 1.04e+02 1.81e-01
gaussian False 6.17e+01 6.52e-01
gaussian True 6.03e+01 6.64e-01
hann False 1.01e+02 2.08e-01
hann True 9.93e+01 2.33e-01
1D evolution simulation in periodic conditions¶
1st temporal order¶
In [11]:
# Schrodinger operator with potential (1D)
# p(x, xi) = xi^2 + V(x)
x = symbols('x', real=True)
xi = symbols('xi', real=True)
expr_schrodinger = -I * xi**2 -1/2* x**2
op_schrodinger = PseudoDifferentialOperator(expr=expr_schrodinger, vars_x=[x], mode='symbol')
# Grids
x_vals = np.linspace(-10, 10, 128)
t_vals = np.linspace(0, 10, 50)
# Initial condition: Gaussian wave packet localized on the left
def ic_gaussian(x):
x0 = -1 # Initial position
k0 = 0 # Initial momentum
sigma = 0.5
norm_factor = (1 / (sigma * np.sqrt(np.pi))) ** 0.5 # sqrt(1 / (σ√π))
return norm_factor * np.exp(-((x - x0) ** 2) / (2 * sigma**2)) * np.exp(1j * k0 * x)
# Simulate
ani_schrod = op_schrodinger.simulate_evolution(
x_grid=x_vals,
t_grid=t_vals,
initial_condition=ic_gaussian,
solver_params={'boundary_condition': 'periodic'} # or 'dirichlet' 'periodic'
)
# To display in a notebook
HTML(ani_schrod.to_jshtml())
symbol =
2 2
- 0.5⋅x - ⅈ⋅ξ
*********************************
* Partial differential equation *
*********************************
∂ ⎛ 2 2 ⎞
──(u(t, x)) = psiOp⎝- 0.5⋅x - ⅈ⋅ξ , u(t, x)⎠
∂t
********************
* Equation parsing *
********************
Equation rewritten in standard form: -psiOp(-0.5*x**2 - I*xi**2, u(t, x)) + Derivative(u(t, x), t)
⚠️ psiOp detected: skipping expansion for safety
Expanded equation: -psiOp(-0.5*x**2 - I*xi**2, u(t, x)) + Derivative(u(t, x), t)
Analyzing term: -psiOp(-0.5*x**2 - I*xi**2, u(t, x))
--> Classified as pseudo linear term (psiOp)
Analyzing term: Derivative(u(t, x), t)
Derivative found: Derivative(u(t, x), t)
--> Classified as linear
Final linear terms: {Derivative(u(t, x), t): 1}
Final nonlinear terms: []
Symbol terms: []
Pseudo terms: [(-1, -0.5*x**2 - I*xi**2)]
Source terms: []
⚠️ Pseudo‑differential operator detected: all other linear terms have been rejected.
*******************************
* Linear operator computation *
*******************************
Characteristic equation before symbol treatment:
-ⅈ⋅ω
--- Symbolic symbol analysis ---
symb_omega: 0
symb_k: 0
Raw characteristic equation:
-ⅈ⋅ω
Temporal order from dispersion relation: 1
self.pseudo_terms = [(-1, -0.5*x**2 - I*xi**2)]
✅ Time derivative coefficient detected: 1
symbol =
2 2
- 0.5⋅x - ⅈ⋅ξ
⚙️ Solving the evolution equation (order 1 in time)...
⚠️ For psiOp, use interactive_symbol_analysis. ******************* * Solving the PDE * *******************
✅ Solving completed. 🎞️ Creating the animation... ********************* * Solution plotting * *********************
/home/fifi/anaconda3/lib/python3.12/site-packages/matplotlib/transforms.py:2875: ComplexWarning: Casting complex values to real discards the imaginary part vmin, vmax = map(float, [vmin, vmax])
Out[11]:
2nd temporal order¶
In [12]:
x = symbols('x', real=True)
xi = symbols('xi', real=True)
expr = -xi**2 - exp(-(x-2)**2) # symbol of the operator ∂ₓ²
op = PseudoDifferentialOperator(expr=expr, vars_x=[x], mode='symbol')
Lx = 10
Nx = 256
x_grid = np.linspace(-Lx/2, Lx/2, Nx)
t_grid = np.linspace(0, 20, 1000)
def ic(x):
return np.exp(-x**2)
def iv(x): # initial velocity set to zero
return np.zeros_like(x)
ani = op.simulate_evolution(
x_grid=x_grid,
t_grid=t_grid,
initial_condition=ic,
initial_velocity=iv, # indicates that this is a 2nd order PDE
solver_params={
'boundary_condition': 'periodic', # 'periodic' or 'dirichlet'
'n_frames': 50
}
)
HTML(ani.to_jshtml())
symbol =
2
2 -(x - 2)
- ξ - ℯ
*********************************
* Partial differential equation *
*********************************
2 ⎛ 2 ⎞
∂ ⎜ 2 -(x - 2) ⎟
───(u(t, x)) = psiOp⎝- ξ - ℯ , u(t, x)⎠
2
∂t
********************
* Equation parsing *
********************
Equation rewritten in standard form: -psiOp(-xi**2 - exp(-(x - 2)**2), u(t, x)) + Derivative(u(t, x), (t, 2))
⚠️ psiOp detected: skipping expansion for safety
Expanded equation: -psiOp(-xi**2 - exp(-(x - 2)**2), u(t, x)) + Derivative(u(t, x), (t, 2))
Analyzing term: -psiOp(-xi**2 - exp(-(x - 2)**2), u(t, x))
--> Classified as pseudo linear term (psiOp)
Analyzing term: Derivative(u(t, x), (t, 2))
Derivative found: Derivative(u(t, x), (t, 2))
--> Classified as linear
Final linear terms: {Derivative(u(t, x), (t, 2)): 1}
Final nonlinear terms: []
Symbol terms: []
Pseudo terms: [(-1, -xi**2 - exp(-(x - 2)**2))]
Source terms: []
⚠️ Pseudo‑differential operator detected: all other linear terms have been rejected.
*******************************
* Linear operator computation *
*******************************
Characteristic equation before symbol treatment:
2
-ω
--- Symbolic symbol analysis ---
symb_omega: 0
symb_k: 0
Raw characteristic equation:
2
-ω
Temporal order from dispersion relation: 2
self.pseudo_terms = [(-1, -xi**2 - exp(-(x - 2)**2))]
✅ Time derivative coefficient detected: 1
symbol =
2
2 -(x - 2)
- ξ - ℯ
⚙️ Solving the evolution equation (order 2 in time)...
⚠️ For psiOp, use interactive_symbol_analysis.
*******************
* Solving the PDE *
*******************
✅ Solving completed. 🎞️ Creating the animation... ********************* * Solution plotting * *********************
Out[12]:
1D evolution simulation in Dirichlet conditions¶
1st temporal order¶
In [13]:
# Schrodinger operator with potential (1D)
# p(x, xi) = xi^2 + V(x)
x = symbols('x', real=True)
xi = symbols('xi', real=True)
expr_schrodinger = -I * xi**2 -1/2* x**2
op_schrodinger = PseudoDifferentialOperator(expr=expr_schrodinger, vars_x=[x], mode='symbol')
# Grids
x_vals = np.linspace(-10, 10, 128)
t_vals = np.linspace(0, 10, 50)
# Initial condition: Gaussian wave packet localized on the left
def ic_gaussian(x):
x0 = -1 # Initial position
k0 = 0 # Initial momentum
sigma = 0.5
norm_factor = (1 / (sigma * np.sqrt(np.pi))) ** 0.5 # sqrt(1 / (σ√π))
return norm_factor * np.exp(-((x - x0) ** 2) / (2 * sigma**2)) * np.exp(1j * k0 * x)
# Simulate
ani_schrod = op_schrodinger.simulate_evolution(
x_grid=x_vals,
t_grid=t_vals,
initial_condition=ic_gaussian,
solver_params={'boundary_condition': 'dirichlet'} # 'dirichlet' or 'periodic'
)
# To display in a notebook
HTML(ani_schrod.to_jshtml())
symbol =
2 2
- 0.5⋅x - ⅈ⋅ξ
*********************************
* Partial differential equation *
*********************************
∂ ⎛ 2 2 ⎞
──(u(t, x)) = psiOp⎝- 0.5⋅x - ⅈ⋅ξ , u(t, x)⎠
∂t
********************
* Equation parsing *
********************
Equation rewritten in standard form: -psiOp(-0.5*x**2 - I*xi**2, u(t, x)) + Derivative(u(t, x), t)
⚠️ psiOp detected: skipping expansion for safety
Expanded equation: -psiOp(-0.5*x**2 - I*xi**2, u(t, x)) + Derivative(u(t, x), t)
Analyzing term: -psiOp(-0.5*x**2 - I*xi**2, u(t, x))
--> Classified as pseudo linear term (psiOp)
Analyzing term: Derivative(u(t, x), t)
Derivative found: Derivative(u(t, x), t)
--> Classified as linear
Final linear terms: {Derivative(u(t, x), t): 1}
Final nonlinear terms: []
Symbol terms: []
Pseudo terms: [(-1, -0.5*x**2 - I*xi**2)]
Source terms: []
⚠️ Pseudo‑differential operator detected: all other linear terms have been rejected.
*******************************
* Linear operator computation *
*******************************
Characteristic equation before symbol treatment:
-ⅈ⋅ω
--- Symbolic symbol analysis ---
symb_omega: 0
symb_k: 0
Raw characteristic equation:
-ⅈ⋅ω
Temporal order from dispersion relation: 1
self.pseudo_terms = [(-1, -0.5*x**2 - I*xi**2)]
✅ Time derivative coefficient detected: 1
symbol =
2 2
- 0.5⋅x - ⅈ⋅ξ
⚙️ Solving the evolution equation (order 1 in time)...
⚠️ For psiOp, use interactive_symbol_analysis.
*******************
* Solving the PDE *
*******************
✅ Solving completed. 🎞️ Creating the animation... ********************* * Solution plotting * *********************
/home/fifi/anaconda3/lib/python3.12/site-packages/matplotlib/cbook.py:1709: ComplexWarning: Casting complex values to real discards the imaginary part return math.isfinite(val) /home/fifi/anaconda3/lib/python3.12/site-packages/matplotlib/transforms.py:2875: ComplexWarning: Casting complex values to real discards the imaginary part vmin, vmax = map(float, [vmin, vmax])
Out[13]:
2nd temporal order¶
In [14]:
x = symbols('x', real=True)
xi = symbols('xi', real=True)
expr = -xi**2 - exp(-(x-2)**2) # symbol of the operator ∂ₓ²
op = PseudoDifferentialOperator(expr=expr, vars_x=[x], mode='symbol')
Lx = 10
Nx = 256
x_grid = np.linspace(-Lx/2, Lx/2, Nx)
t_grid = np.linspace(0, 20, 1000)
def ic(x):
return np.exp(-x**2)
def iv(x): # initial velocity set to zero
return np.zeros_like(x)
ani = op.simulate_evolution(
x_grid=x_grid,
t_grid=t_grid,
initial_condition=ic,
initial_velocity=iv, # indicates that this is a 2nd order PDE
solver_params={
'boundary_condition': 'dirichlet', # 'periodic' or 'dirichlet'
'n_frames': 50
}
)
HTML(ani.to_jshtml())
symbol =
2
2 -(x - 2)
- ξ - ℯ
*********************************
* Partial differential equation *
*********************************
2 ⎛ 2 ⎞
∂ ⎜ 2 -(x - 2) ⎟
───(u(t, x)) = psiOp⎝- ξ - ℯ , u(t, x)⎠
2
∂t
********************
* Equation parsing *
********************
Equation rewritten in standard form: -psiOp(-xi**2 - exp(-(x - 2)**2), u(t, x)) + Derivative(u(t, x), (t, 2))
⚠️ psiOp detected: skipping expansion for safety
Expanded equation: -psiOp(-xi**2 - exp(-(x - 2)**2), u(t, x)) + Derivative(u(t, x), (t, 2))
Analyzing term: -psiOp(-xi**2 - exp(-(x - 2)**2), u(t, x))
--> Classified as pseudo linear term (psiOp)
Analyzing term: Derivative(u(t, x), (t, 2))
Derivative found: Derivative(u(t, x), (t, 2))
--> Classified as linear
Final linear terms: {Derivative(u(t, x), (t, 2)): 1}
Final nonlinear terms: []
Symbol terms: []
Pseudo terms: [(-1, -xi**2 - exp(-(x - 2)**2))]
Source terms: []
⚠️ Pseudo‑differential operator detected: all other linear terms have been rejected.
*******************************
* Linear operator computation *
*******************************
Characteristic equation before symbol treatment:
2
-ω
--- Symbolic symbol analysis ---
symb_omega: 0
symb_k: 0
Raw characteristic equation:
2
-ω
Temporal order from dispersion relation: 2
self.pseudo_terms = [(-1, -xi**2 - exp(-(x - 2)**2))]
✅ Time derivative coefficient detected: 1
symbol =
2
2 -(x - 2)
- ξ - ℯ
⚙️ Solving the evolution equation (order 2 in time)...
⚠️ For psiOp, use interactive_symbol_analysis.
*******************
* Solving the PDE *
*******************
✅ Solving completed. 🎞️ Creating the animation... ********************* * Solution plotting * *********************
Out[14]:
2D evolution simulation in periodic conditions¶
1st temporal order¶
In [15]:
# Symbolic variables
x, y = symbols('x y', real=True)
xi, eta = symbols('xi eta', real=True)
# Schrödinger symbol with harmonic potential
# expr = -1j * (xi**2 + eta**2) - 0.5 * (x**2 + y**2)
expr = -1j * (xi**2 + eta**2) - (0.5 * x**2 + 0.2 * y**2)
op_schrod_2d = PseudoDifferentialOperator(expr=expr, vars_x=[x, y], mode='symbol')
# Grids
x_vals = np.linspace(-6, 6, 32)
y_vals = np.linspace(-6, 6, 32)
t_vals = np.linspace(0, 6, 60)
# Initial condition: Gaussian packet centered at (x0, y0)
def ic_gaussian_2d(x, y):
x0, y0 = -2.0, 0.0
sigma = 0.5
norm = 1 / (2 * np.pi * sigma**2)
return norm * np.exp(-((x - x0)**2 + (y - y0)**2) / (2 * sigma**2))
# Simulation
ani = op_schrod_2d.simulate_evolution(
x_grid=x_vals,
y_grid=y_vals,
t_grid=t_vals,
initial_condition=ic_gaussian_2d,
component='abs',
solver_params={
'boundary_condition': 'periodic', # 'dirichlet' or 'periodic' for reflection
'n_frames': 50
}
)
# Display in a Jupyter notebook
HTML(ani.to_jshtml())
symbol =
2 2 ⎛ 2 2⎞
- 0.5⋅x - 0.2⋅y - 1.0⋅ⅈ⋅⎝η + ξ ⎠
*********************************
* Partial differential equation *
*********************************
∂ ⎛ 2 2 ⎛ 2 2⎞ ⎞
──(u(t, x, y)) = psiOp⎝- 0.5⋅x - 0.2⋅y - 1.0⋅ⅈ⋅⎝η + ξ ⎠, u(t, x, y)⎠
∂t
********************
* Equation parsing *
********************
Equation rewritten in standard form: -psiOp(-0.5*x**2 - 0.2*y**2 - 1.0*I*(eta**2 + xi**2), u(t, x, y)) + Derivative(u(t, x, y), t)
⚠️ psiOp detected: skipping expansion for safety
Expanded equation: -psiOp(-0.5*x**2 - 0.2*y**2 - 1.0*I*(eta**2 + xi**2), u(t, x, y)) + Derivative(u(t, x, y), t)
Analyzing term: -psiOp(-0.5*x**2 - 0.2*y**2 - 1.0*I*(eta**2 + xi**2), u(t, x, y))
--> Classified as pseudo linear term (psiOp)
Analyzing term: Derivative(u(t, x, y), t)
Derivative found: Derivative(u(t, x, y), t)
--> Classified as linear
Final linear terms: {Derivative(u(t, x, y), t): 1}
Final nonlinear terms: []
Symbol terms: []
Pseudo terms: [(-1, -0.5*x**2 - 0.2*y**2 - 1.0*I*(eta**2 + xi**2))]
Source terms: []
⚠️ Pseudo‑differential operator detected: all other linear terms have been rejected.
*******************************
* Linear operator computation *
*******************************
Characteristic equation before symbol treatment:
-ⅈ⋅ω
--- Symbolic symbol analysis ---
symb_omega: 0
symb_k: 0
Raw characteristic equation:
-ⅈ⋅ω
Temporal order from dispersion relation: 1
self.pseudo_terms = [(-1, -0.5*x**2 - 0.2*y**2 - 1.0*I*(eta**2 + xi**2))]
✅ Time derivative coefficient detected: 1
symbol =
2 2 ⎛ 2 2⎞
- 0.5⋅x - 0.2⋅y - 1.0⋅ⅈ⋅⎝η + ξ ⎠
⚙️ Solving the evolution equation (order 1 in time)...
⚠️ For psiOp, use interactive_symbol_analysis. ******************* * Solving the PDE * *******************
✅ Solving completed. 🎞️ Creating the animation... ********************* * Solution plotting * *********************
Out[15]:
2nd temporal order¶
In [16]:
# Symbolic variables
x, y = symbols('x y', real=True)
xi, eta = symbols('xi eta', real=True)
# Symbol: Laplacian + localized potential
expr = - (xi**2 + eta**2) - exp(-(x - 2)**2 - y**2)
op = PseudoDifferentialOperator(expr=expr, vars_x=[x, y], mode='symbol')
# Spatial and temporal grids
Lx, Ly = 10, 10
Nx, Ny = 32, 32
Nt = 500
Tmax = 20.0
x_grid = np.linspace(-Lx/2, Lx/2, Nx)
y_grid = np.linspace(-Ly/2, Ly/2, Ny)
t_grid = np.linspace(0, Tmax, Nt)
# Initial condition: Gaussian bump centered at (0,0)
def ic_2d(x, y):
return np.exp(-(x**2 + y**2))
# Initial velocity: zero everywhere
def iv_2d(x, y):
return np.zeros_like(x)
# Run simulation
ani = op.simulate_evolution(
x_grid=x_grid,
y_grid=y_grid,
t_grid=t_grid,
initial_condition=ic_2d,
initial_velocity=iv_2d, # triggers 2nd-order evolution
solver_params={
'boundary_condition': 'periodic', # try also 'dirichlet'
'n_frames': 50
}
)
# Display animation (e.g., in a Jupyter notebook)
HTML(ani.to_jshtml())
symbol =
2 2
2 2 - y - (x - 2)
- η - ξ - ℯ
*********************************
* Partial differential equation *
*********************************
2 ⎛ 2 2 ⎞
∂ ⎜ 2 2 - y - (x - 2) ⎟
───(u(t, x, y)) = psiOp⎝- η - ξ - ℯ , u(t, x, y)⎠
2
∂t
********************
* Equation parsing *
********************
Equation rewritten in standard form: -psiOp(-eta**2 - xi**2 - exp(-y**2 - (x - 2)**2), u(t, x, y)) + Derivative(u(t, x, y), (t, 2))
⚠️ psiOp detected: skipping expansion for safety
Expanded equation: -psiOp(-eta**2 - xi**2 - exp(-y**2 - (x - 2)**2), u(t, x, y)) + Derivative(u(t, x, y), (t, 2))
Analyzing term: -psiOp(-eta**2 - xi**2 - exp(-y**2 - (x - 2)**2), u(t, x, y))
--> Classified as pseudo linear term (psiOp)
Analyzing term: Derivative(u(t, x, y), (t, 2))
Derivative found: Derivative(u(t, x, y), (t, 2))
--> Classified as linear
Final linear terms: {Derivative(u(t, x, y), (t, 2)): 1}
Final nonlinear terms: []
Symbol terms: []
Pseudo terms: [(-1, -eta**2 - xi**2 - exp(-y**2 - (x - 2)**2))]
Source terms: []
⚠️ Pseudo‑differential operator detected: all other linear terms have been rejected.
*******************************
* Linear operator computation *
*******************************
Characteristic equation before symbol treatment:
2
-ω
--- Symbolic symbol analysis ---
symb_omega: 0
symb_k: 0
Raw characteristic equation:
2
-ω
Temporal order from dispersion relation: 2
self.pseudo_terms = [(-1, -eta**2 - xi**2 - exp(-y**2 - (x - 2)**2))]
✅ Time derivative coefficient detected: 1
symbol =
2 2
2 2 - y - (x - 2)
- η - ξ - ℯ
⚙️ Solving the evolution equation (order 2 in time)...
⚠️ For psiOp, use interactive_symbol_analysis. ******************* * Solving the PDE * *******************
✅ Solving completed. 🎞️ Creating the animation... ********************* * Solution plotting * *********************
Out[16]:
2D evolution simulation in Dirichlet conditions¶
1st temporal order¶
In [17]:
# Symbolic variables
x, y = symbols('x y', real=True)
xi, eta = symbols('xi eta', real=True)
# Schrödinger symbol with harmonic potential
# expr = -1j * (xi**2 + eta**2) - 0.5 * (x**2 + y**2)
expr = -1j * (xi**2 + eta**2) - (0.5 * x**2 + 0.2 * y**2)
op_schrod_2d = PseudoDifferentialOperator(expr=expr, vars_x=[x, y], mode='symbol')
# Grids
x_vals = np.linspace(-6, 6, 32)
y_vals = np.linspace(-6, 6, 32)
t_vals = np.linspace(0, 6, 60)
# Initial condition: Gaussian packet centered at (x0, y0)
def ic_gaussian_2d(x, y):
x0, y0 = -2.0, 0.0
sigma = 0.5
norm = 1 / (2 * np.pi * sigma**2)
return norm * np.exp(-((x - x0)**2 + (y - y0)**2) / (2 * sigma**2))
# Simulation
ani = op_schrod_2d.simulate_evolution(
x_grid=x_vals,
y_grid=y_vals,
t_grid=t_vals,
initial_condition=ic_gaussian_2d,
component='abs',
solver_params={
'boundary_condition': 'dirichlet', # 'dirichlet' or 'periodic' for reflection
'n_frames': 50
}
)
# Display in a Jupyter notebook
HTML(ani.to_jshtml())
symbol =
2 2 ⎛ 2 2⎞
- 0.5⋅x - 0.2⋅y - 1.0⋅ⅈ⋅⎝η + ξ ⎠
*********************************
* Partial differential equation *
*********************************
∂ ⎛ 2 2 ⎛ 2 2⎞ ⎞
──(u(t, x, y)) = psiOp⎝- 0.5⋅x - 0.2⋅y - 1.0⋅ⅈ⋅⎝η + ξ ⎠, u(t, x, y)⎠
∂t
********************
* Equation parsing *
********************
Equation rewritten in standard form: -psiOp(-0.5*x**2 - 0.2*y**2 - 1.0*I*(eta**2 + xi**2), u(t, x, y)) + Derivative(u(t, x, y), t)
⚠️ psiOp detected: skipping expansion for safety
Expanded equation: -psiOp(-0.5*x**2 - 0.2*y**2 - 1.0*I*(eta**2 + xi**2), u(t, x, y)) + Derivative(u(t, x, y), t)
Analyzing term: -psiOp(-0.5*x**2 - 0.2*y**2 - 1.0*I*(eta**2 + xi**2), u(t, x, y))
--> Classified as pseudo linear term (psiOp)
Analyzing term: Derivative(u(t, x, y), t)
Derivative found: Derivative(u(t, x, y), t)
--> Classified as linear
Final linear terms: {Derivative(u(t, x, y), t): 1}
Final nonlinear terms: []
Symbol terms: []
Pseudo terms: [(-1, -0.5*x**2 - 0.2*y**2 - 1.0*I*(eta**2 + xi**2))]
Source terms: []
⚠️ Pseudo‑differential operator detected: all other linear terms have been rejected.
*******************************
* Linear operator computation *
*******************************
Characteristic equation before symbol treatment:
-ⅈ⋅ω
--- Symbolic symbol analysis ---
symb_omega: 0
symb_k: 0
Raw characteristic equation:
-ⅈ⋅ω
Temporal order from dispersion relation: 1
self.pseudo_terms = [(-1, -0.5*x**2 - 0.2*y**2 - 1.0*I*(eta**2 + xi**2))]
✅ Time derivative coefficient detected: 1
symbol =
2 2 ⎛ 2 2⎞
- 0.5⋅x - 0.2⋅y - 1.0⋅ⅈ⋅⎝η + ξ ⎠
⚙️ Solving the evolution equation (order 1 in time)...
⚠️ For psiOp, use interactive_symbol_analysis. ******************* * Solving the PDE * *******************
✅ Solving completed. 🎞️ Creating the animation... ********************* * Solution plotting * *********************
Out[17]:
2nd temporal order¶
In [18]:
# Symbolic variables
x, y = symbols('x y', real=True)
xi, eta = symbols('xi eta', real=True)
# Symbol: Laplacian + localized potential
expr = - (xi**2 + eta**2) - exp(-(x - 2)**2 - y**2)
op = PseudoDifferentialOperator(expr=expr, vars_x=[x, y], mode='symbol')
# Spatial and temporal grids
Lx, Ly = 10, 10
Nx, Ny = 32, 32
Nt = 500
Tmax = 20.0
x_grid = np.linspace(-Lx/2, Lx/2, Nx)
y_grid = np.linspace(-Ly/2, Ly/2, Ny)
t_grid = np.linspace(0, Tmax, Nt)
# Initial condition: Gaussian bump centered at (0,0)
def ic_2d(x, y):
return np.exp(-(x**2 + y**2))
# Initial velocity: zero everywhere
def iv_2d(x, y):
return np.zeros_like(x)
# Run simulation
ani = op.simulate_evolution(
x_grid=x_grid,
y_grid=y_grid,
t_grid=t_grid,
initial_condition=ic_2d,
initial_velocity=iv_2d, # triggers 2nd-order evolution
solver_params={
'boundary_condition': 'dirichlet', # 'periodic' or 'dirichlet'
'n_frames': 50
}
)
# Display animation (e.g., in a Jupyter notebook)
HTML(ani.to_jshtml())
symbol =
2 2
2 2 - y - (x - 2)
- η - ξ - ℯ
*********************************
* Partial differential equation *
*********************************
2 ⎛ 2 2 ⎞
∂ ⎜ 2 2 - y - (x - 2) ⎟
───(u(t, x, y)) = psiOp⎝- η - ξ - ℯ , u(t, x, y)⎠
2
∂t
********************
* Equation parsing *
********************
Equation rewritten in standard form: -psiOp(-eta**2 - xi**2 - exp(-y**2 - (x - 2)**2), u(t, x, y)) + Derivative(u(t, x, y), (t, 2))
⚠️ psiOp detected: skipping expansion for safety
Expanded equation: -psiOp(-eta**2 - xi**2 - exp(-y**2 - (x - 2)**2), u(t, x, y)) + Derivative(u(t, x, y), (t, 2))
Analyzing term: -psiOp(-eta**2 - xi**2 - exp(-y**2 - (x - 2)**2), u(t, x, y))
--> Classified as pseudo linear term (psiOp)
Analyzing term: Derivative(u(t, x, y), (t, 2))
Derivative found: Derivative(u(t, x, y), (t, 2))
--> Classified as linear
Final linear terms: {Derivative(u(t, x, y), (t, 2)): 1}
Final nonlinear terms: []
Symbol terms: []
Pseudo terms: [(-1, -eta**2 - xi**2 - exp(-y**2 - (x - 2)**2))]
Source terms: []
⚠️ Pseudo‑differential operator detected: all other linear terms have been rejected.
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* Linear operator computation *
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Characteristic equation before symbol treatment:
2
-ω
--- Symbolic symbol analysis ---
symb_omega: 0
symb_k: 0
Raw characteristic equation:
2
-ω
Temporal order from dispersion relation: 2
self.pseudo_terms = [(-1, -eta**2 - xi**2 - exp(-y**2 - (x - 2)**2))]
✅ Time derivative coefficient detected: 1
symbol =
2 2
2 2 - y - (x - 2)
- η - ξ - ℯ
⚙️ Solving the evolution equation (order 2 in time)...
⚠️ For psiOp, use interactive_symbol_analysis. ******************* * Solving the PDE * *******************
✅ Solving completed. 🎞️ Creating the animation... ********************* * Solution plotting * *********************
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