def SchemeUpwind(u, A, omega, D, rhs, bc):
"""
Discretization of -Tr(A(x) hess u(x)) + \| grad u(x) - omega(x) \|_D(x)^2 - rhs,
with Dirichlet boundary conditions, using upwind finite differences for the first order part.
The scheme is degenerate elliptic if A and D are positive definite.
"""
# Compute the decompositions (here offset_e = offset_f)
nothing = (np.full((0, ), 0.), np.full((2, 0),
0)) # empty coefs and offsets
mu, offset_e = nothing if A is None else Selling.Decomposition(A)
nu, offset_f = nothing if D is None else Selling.Decomposition(D)
omega_f = lp.dot_VA(omega, offset_f.astype(float))
# First and second order finite differences
maxi = np.maximum
mu, nu, omega_f = (bc.as_field(e) for e in (mu, nu, omega_f))
dup = bc.DiffUpwind(u, offset_f)
dum = bc.DiffUpwind(u, -offset_f)
dup[...,
bc.not_interior] = 0. # Placeholder values to silence NaN warnings
dum[..., bc.not_interior] = 0.
d2u = bc.Diff2(u, offset_e)
# Scheme in the interior
du = maxi(0., maxi(omega_f - dup, -omega_f - dum))
residue = -lp.dot_VV(mu, d2u) + lp.dot_VV(nu, du**2) - rhs
# Placeholders outside domain
return np.where(bc.interior, residue, u - bc.grid_values)
def SchemeCentered(u, cst, mult, omega, diff, bc, ret_hmax=False):
"""Discretization of a linear non-divergence form second order PDE
cst + mult u + <omega,grad u>- tr(diff hess(u)) = 0
Second order accurate, centered yet monotone finite differences are used for <omega,grad u>
- bc : boundary conditions.
- ret_hmax : return the largest grid scale for which monotony holds
"""
# Decompose the tensor field
coefs2, offsets = Selling.Decomposition(diff)
# Decompose the vector field
scals = lp.dot_VA(lp.solve_AV(diff, omega), offsets.astype(float))
coefs1 = coefs2 * scals
if ret_hmax: return 2. / norm(scals, ord=np.inf)
# Compute the first and second order finite differences
du = bc.DiffCentered(u, offsets)
d2u = bc.Diff2(u, offsets)
# In interior : cst + mult u + <omega,grad u>- tr(diff hess(u)) = 0
coefs1, coefs2 = (bc.as_field(e) for e in (coefs1, coefs2))
residue = cst + mult * u + lp.dot_VV(coefs1, du) - lp.dot_VV(coefs2, d2u)
# On boundary : u-bc = 0
return np.where(bc.interior, residue, u - bc.grid_values)
def SchemeNonlinear(u, x, f, bc):
coef, offsets = Selling.Decomposition(D(x))
du = bc.DiffCentered(u, offsets)
d2u = bc.Diff2(u, offsets)
p = lp.dot_AV(lp.inverse(D(x)), np.sum(coef * du * offsets, axis=1))
return np.where(
bc.interior,
-1 / 2 * lp.dot_VV(omega(x), p)**2 - lp.dot_VV(coef, d2u) - f,
u - bc.grid_values,
)
def sigma_reflector(x, r, e):
tmp = (2 * (r**3 + r**5 + r**5 + lp.dot_VV(x, x)) * e -
4 * r**5 * lp.dot_VV(e, x) * x)
y = (4 * r**5 * lp.dot_VV(lp.perp(e), x) * lp.perp(tmp) + np.sqrt(
lp.dot_VV(tmp, tmp) - 16 * r**10 * lp.dot_VV(lp.perp(e), x)**2) *
tmp) / np.stack([lp.dot_VV(tmp, tmp),
lp.dot_VV(tmp, tmp)])
return 2 * r**3 * lp.dot_VV(e,
y - x) / (1 + r**2 * lp.dot_VV(x - y, x - y))
def SchemeUniform(u, SB, f, bc):
# Compute the finite differences along the superbase directions
d2u = bc.Diff2(u, SB)
d2u[..., bc.not_interior] = 0. # Placeholder value to silent NaN warnings
# Generate the parameters for the low dimensional optimization problem
Q = 0.5 * np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]])
l = -d2u
m = lp.dot_VV(SB, SB)
# Evaluate the numerical scheme
m = bc.as_field(m)
from agd.FiniteDifferences import as_field
Q = as_field(Q, m.shape[1:])
dim = 2
alpha = dim * f**(1 / dim)
mask = (alpha == 0)
Q = Q * np.where(mask, 1., alpha**2)
residue = ConstrainedMaximize(Q, l, m).max(axis=0)
residue[mask] = np.max(l / m, axis=0).max(axis=0)[mask]
# Boundary conditions
return ad.where(bc.interior, residue, u - bc.grid_values)
def SchemeUniform_OptInner(u, SB, f, bc, oracle=None):
# Use the oracle, if available, to select the active superbases only
if not (oracle is None):
SB = np.take_along_axis(SB,
np.broadcast_to(
oracle,
SB.shape[:2] + (1, ) + oracle.shape),
axis=2)
d2u = bc.Diff2(u, SB)
d2u[..., bc.not_interior] = 0. # Placeholder value to silent NaN warnings
# Generate the parameters for the low dimensional optimization problem
Q = 0.5 * np.array([[0, 1, 1], [1, 0, 1], [1, 1, 0]])
dim = 2
l = -d2u
m = lp.dot_VV(SB, SB)
m = bc.as_field(m)
from agd.FiniteDifferences import as_field
Q = as_field(Q, m.shape[1:])
dim = 2
alpha = dim * f**(1 / dim)
mask = (alpha == 0)
Q = Q * np.where(mask, 1., alpha**2)
# Evaluate the non-linear functional using dense-sparse composition
residue = ad.apply(ConstrainedMaximize, Q, l, m,
shape_bound=u.shape).copy()
residue[:, mask] = np.max(l / m, axis=0)[:, mask]
return ad.max_argmax(residue, axis=0)
def SolveNonlinear(x, f, bc):
dde = True
def Solver(residue):
nonlocal dde
triplets, rhs = residue.solve(raw=True)
mat = tocsr(triplets)
# if (diags(mat.diagonal()) - mat).min() <= -1e-8:
# dde = False
dde = (diags(mat.diagonal()) - mat).min() > -1e-8
precond = diags(1 / mat.diagonal())
matprecond = precond @ mat
rhsprecond = precond @ rhs
return spsolve(matprecond, rhsprecond).reshape(x.shape[1:])
result = newton_root(SchemeNonlinear,
0.0001 * lp.dot_VV(x, x),
params=(x, f, bc),
solver=Solver)
return result, dde
def MinimizeTrace_Opt(u, alpha, bc, oracle=None):
if oracle is None: return MinimizeTrace(u, alpha, bc)
# The oracle contains the optimal angles
diffs = Diff(alpha, oracle.squeeze(axis=0))
coefs, sb = Selling.Decomposition(diffs)
value = lp.dot_VV(coefs, bc.Diff2(u, sb))
return value, oracle
def SchemeUpwind(u, cst, mult, omega, diff, bc):
"""Discretization of a linear non-divergence form second order PDE
cst + mult u + <omega,grad u>- tr(diff hess(u)) = 0
First order accurate, upwind finite differences are used for <omega,grad u>
- bc : boundary conditions.
"""
# Decompose the tensor field
coefs2, offsets2 = Selling.Decomposition(diff)
omega, coefs2 = (bc.as_field(e) for e in (omega, coefs2))
# Decompose the vector field
coefs1 = -np.abs(omega)
basis = bc.as_field(np.eye(len(omega)))
offsets1 = -np.sign(omega) * basis
# Compute the first and second order finite differences
du = bc.DiffUpwind(u, offsets1.astype(int))
d2u = bc.Diff2(u, offsets2)
# In interior : cst + mult u + <omega,grad u>- tr(diff hess(u)) = 0
residue = cst + mult * u + lp.dot_VV(coefs1, du) - lp.dot_VV(coefs2, d2u)
# On boundary : u-bc = 0
return np.where(bc.interior, residue, u - bc.grid_values)
def MinimizeTrace(u, alpha, bc, sqrt_relax=1e-16):
# Compute the tensor decompositions
D = MakeD(alpha)
theta, sb = AnglesAndSuperbases(D)
theta = np.array([theta[:-1], theta[1:]])
# Compute the second order differences in the direction orthogonal to the superbase
sb_rotated = np.array([-sb[1], sb[0]])
d2u = bc.Diff2(u, sb_rotated)
d2u[..., bc.not_interior] = 0. # Placeholder values to silent NaNs
# Compute the coefficients of the tensor decompositions
sb1, sb2 = np.roll(sb, 1, axis=1), np.roll(sb, 2, axis=1)
sb1, sb2 = (e.reshape((2, 3, 1) + sb.shape[2:]) for e in (sb1, sb2))
D = D.reshape((2, 2, 1, 3, 1) + D.shape[3:])
# Axes of D are space,space,index of superbase element, index of D, index of superbase, and possibly shape of u
scals = lp.dot_VAV(sb1, D, sb2)
# Compute the coefficients of the trigonometric polynomial
scals, theta = (bc.as_field(e) for e in (scals, theta))
coefs = -lp.dot_VV(scals, np.expand_dims(d2u, axis=1))
# Optimality condition for the trigonometric polynomial in the interior
value = coefs[0] - np.sqrt(
np.maximum(coefs[1]**2 + coefs[2]**2, sqrt_relax))
coefs_ = np.array(coefs) # removed AD information
angle = np.arctan2(-coefs_[2], -coefs_[1]) / 2.
angle[angle < 0] += np.pi
# Boundary conditions for the trigonometric polynomial minimization
mask = np.logical_not(np.logical_and(theta[0] <= angle, angle <= theta[1]))
t, c = theta[:, mask], coefs[:, mask]
value[mask], amin_t = ad.min_argmin(c[0] + c[1] * np.cos(2 * t) +
c[2] * np.sin(2 * t),
axis=0)
# Minimize over superbases
value, amin_sb = ad.min_argmin(value, axis=0)
# Record the optimal angles for future use
angle[mask] = np.take_along_axis(t, np.expand_dims(amin_t, axis=0),
axis=0).squeeze(axis=0) # Min over bc
angle = np.take_along_axis(angle, np.expand_dims(amin_sb, axis=0),
axis=0) # Min over superbases
return value, angle
def SchemeLinear(u, x, f, bc):
coef, offsets = Selling.Decomposition(D(x))
# coef_min = np.min(coef)
# offsets_norm2 = lp.dot_VV(offsets, offsets)
# offsets_max2 = np.max(np.where(coef < 1e-13, 0, offsets_norm2))
# print(f"h: {bc.gridscale}, c: {coef_min}, e2: {offsets_max2}")
du = bc.DiffCentered(u, offsets)
d2u = bc.Diff2(u, offsets)
return np.where(
bc.interior,
-lp.dot_VAV(omega(x), lp.inverse(D(x)),
np.sum(coef * du * offsets, axis=1)) -
lp.dot_VV(coef, d2u) - f,
u - bc.grid_values,
)
def SchemeSampling_OptInner(u, diffs, bc, oracle=None):
# Select the active tensors, if they are known
if not (oracle is None):
diffs = np.take_along_axis(diffs,
np.broadcast_to(
oracle,
diffs.shape[:2] + (1, ) + oracle.shape),
axis=2)
print("Has AD information :", ad.is_ad(u),
". Number active tensors per point :", diffs.shape[2])
# Tensor decomposition
coefs, offsets = Selling.Decomposition(diffs)
# Return the minimal value, and the minimizing index
return ad.min_argmin(lp.dot_VV(coefs, bc.Diff2(u, offsets)), axis=0)
def SchemeLaxFriedrichs(u, A, F, bc):
"""
Discretization of - Tr(A(x) hess u(x)) + F(grad u(x)) - 1 = 0,
with Dirichlet boundary conditions. The scheme is second order,
and degenerate elliptic under suitable assumptions.
"""
# Compute the tensor decomposition
coefs, offsets = Selling.Decomposition(A)
A, coefs, offsets = (bc.as_field(e) for e in (A, coefs, offsets))
# Obtain the first and second order finite differences
grad = Gradient(u, A, bc, decomp=(coefs, offsets))
d2u = bc.Diff2(u, offsets)
# Numerical scheme in interior
residue = -lp.dot_VV(coefs, d2u) + F(grad) - 1.
# Placeholders outside domain
return ad.where(bc.interior, residue, u - bc.grid_values)
def EqLinear(u_func, x):
x_ad = Dense2.identity(constant=x, shape_free=x.shape[:1])
u_ad = u_func(x_ad)
du = np.moveaxis(u_ad.coef1, -1, 0)
d2u = np.moveaxis(u_ad.coef2, [-2, -1], [0, 1])
return -lp.dot_VV(omega(x), du) - lp.trace(lp.dot_AA(D(x), d2u))
def u3(x):
d = x.shape[0]
return np.where(lp.dot_VV(x, x) < d, np.sqrt(d - lp.dot_VV(x, x)), 0)
def StencilForConditioning(cond):
V3 = Selling.SuperbasesForConditioning(cond)
offsets = V3.reshape((2, -1))
# Make offsets positive for the lexicographic order, inversing their sign if needed.
offsets[:, offsets[0] < 0] *= -1
offsets[:, np.logical_and(offsets[0] == 0, offsets[1] < 0)] *= -1
V1, indices = np.unique(offsets, axis=1, return_inverse=True)
V3_indices = indices.reshape(V3.shape[1:])
V2_indices = np.unique(
np.sort(
np.concatenate(
(V3_indices[[0, 1]], V3_indices[[0, 2]], V3_indices[[1, 2]]),
axis=1),
axis=0,
),
axis=1,
)
V2 = V1[:, V2_indices]
Q = np.zeros((3, 3, V3.shape[2]))
w = np.zeros((3, V3.shape[2]))
for i in range(3):
Q[i, i] = (lp.dot_VV(V3[:, (i + 1) % 3], V3[:, (i + 1) % 3]) *
lp.dot_VV(V3[:, (i + 2) % 3], V3[:, (i + 2) % 3]) / 4)
Q[i, (i + 1) % 3] = (lp.dot_VV(V3[:, i], V3[:, (i + 1) % 3]) *
lp.dot_VV(V3[:,
(i + 2) % 3], V3[:,
(i + 2) % 3]) / 4)
Q[i, (i + 2) % 3] = (lp.dot_VV(V3[:, i], V3[:, (i + 2) % 3]) *
lp.dot_VV(V3[:,
(i + 1) % 3], V3[:,
(i + 1) % 3]) / 4)
w[i] = -lp.dot_VV(V3[:, (i + 1) % 3], V3[:, (i + 2) % 3]) / 2
omega0 = 1 / (lp.dot_VV(V2[:, 0], V2[:, 0]) *
lp.dot_VV(V2[:, 1], V2[:, 1]))
omega1 = 1 / (2 * np.stack(
[lp.dot_VV(V2[:, 0], V2[:, 0]), -lp.dot_VV(V2[:, 1], V2[:, 1])]))
omega2 = 1 / (2 * np.stack(
[lp.dot_VV(V2[:, 0], V2[:, 0]),
lp.dot_VV(V2[:, 1], V2[:, 1])]))
return Stencil(V1, V2, V2_indices, V3, V3_indices, Q, w, omega0, omega1,
omega2)
interior = domain_ball.level(x) < 0
y = Y_reflector(x[:, interior], u[interior], du[:, interior])
z = Z_reflector(x[:, interior], u[interior], du[:, interior])
plt.tripcolor(*y, z)
plt.show()
# %%
simulate_reflector(y, z)
# %%
u = newton_root(
SchemeBV2Alt,
np.where(
domain_ball.level(x) > np.min(domain_ball.level(x)),
0.1 + 0.001 * lp.dot_VV(x, x),
1,
),
(x, domain_ball, A_reflector, B_reflector, 0.1, F_reflector, stencil),
)
u = np.where(domain_ball.level(x) > np.min(domain_ball.level(x)), u, 0.1)
plt.contourf(*x, np.where(domain_ball.level(x) < 0, u, np.nan))
plt.show()
# %%
gridscale = x[0, 1, 0] - x[0, 0, 0]
du = fd.DiffCentered(u, [[1, 0], [0, 1]], gridscale)
interior = domain_ball.level(x) < 0
y = Y_reflector(x[:, interior], u[interior], du[:, interior])
def sigma_reflector2(x, r, e):
return alpha * np.sqrt(lp.dot_VV(e, e))
def H3(Q, w, b, delta):
Q_delta = lp.dot_AV(Q, delta)
r = np.sqrt(b + lp.dot_VV(delta, Q_delta))
return np.where(np.all(Q_delta <= r * w, axis=0), r - lp.dot_VV(w, delta),
-np.inf)
def f2(x):
return (4 * alpha**2 * (1 + alpha**2 * lp.dot_VV(x, x)) /
(1 - alpha**2 * lp.dot_VV(x, x))**3) * f(
2 * alpha * x / (1 - alpha**2 * lp.dot_VV(x, x)))
def ExactQuartic(x):
return lp.dot_VV(x, x)**2
def B_reflector(x, r, p):
tmp = 1 + np.sqrt(1 - lp.dot_VV(p, p) / r**4)
return r**6 * (tmp**3 - tmp**2) * f(x)
def H2(omega0, omega1, omega2, b, delta):
return np.sqrt(omega0 * b + lp.dot_VV(omega1, delta)**2) - lp.dot_VV(
omega2, delta)
def A_reflector(x, r, p):
tmp = 1 + np.sqrt(1 - lp.dot_VV(p, p) / r**4)
return (2 + tmp) / r * lp.outer(p, p) - r**3 * tmp * lp.identity(
x.shape[1:])
def Z_reflector(x, r, p):
tmp = 1 + np.sqrt(1 - lp.dot_VV(p, p) / r**4)
return (1 - 1 / tmp) / r
def H1(v, delta):
return -delta / lp.dot_VV(v, v)
def u2(x):
return np.maximum(0, np.sqrt(lp.dot_VV(x, x)) - 0.4)**2.5
def Z_reflector2(x, r, p):
return lp.dot_VV(x, p) - r
def Y_reflector(x, r, p):
tmp = 1 + np.sqrt(1 - lp.dot_VV(p, p) / r**4)
return x + 1 / (r**3 * tmp) * p
def B_quartic(x, r, p):
return 48 * lp.dot_VV(x, x)**2
