当然可以
```python
from dolfin import *
from mshr import *
import matplotlib.pyplot as plt
import numpy as np

# 定义网格和几何形状
L = 1.0  # 域的长度
H = 0.1  # 域的高度
Nx = 50  # X方向上的元素数
Ny = 10  # Y方向上的元素数

mesh = RectangleMesh(Point(0, 0), Point(L, H), Nx, Ny)
plot(mesh)

# 定义函数空间
V = FunctionSpace(mesh, "P", 1)  # 标量函数空间
V_vector = VectorFunctionSpace(mesh, "P", 1)  # 向量函数

# 定义边界条件
def left(x, on_boundary):
    return on_boundary and near(x[0], 0.0)

def right(x, on_boundary):
    return on_boundary and near(x[0], L)

u_left = Constant(0.0)
u_right = Expression("t", t=0.0, degree=1)
bc_left = DirichletBC(V_vector.sub(0), u_left, left)
bc_right = DirichletBC(V_vector.sub(0), u_right, right)
bcs = [bc_left, bc_right]

#定义优化参数
E = 1.0  # 杨氏模量
nu = 0.3  # 泊松比

# 界定优化问题
u = TrialFunction(V_vector)
v = TestFunction(V_vector)

rho = Function(V)
rho.assign(Constant(1.0))  # 最初的材料分配

mu = E / (2 * (1 + nu))
lmbda = E * nu / ((1 + nu) * (1 - 2 * nu))
def epsilon(u):
    return 0.5 * (grad(u) + grad(u).T)

def sigma(u):
    return lmbda * div(u) * Identity(2) + 2 * mu * epsilon(u)

F = inner(sigma(u), epsilon(v)) * dx

#定义时间步进参数
dt = 0.1  # 步进
T = 1.0  # 结束时间
t = 0.0  # 当前时间

# Time-stepping loop
u_sol = Function(V_vector)
while t <= T:
    # 更新边界条件
    u_right.t = t

    # 解决优化问题
    solve(lhs(F) == rhs(F), u_sol, bcs)

    # 更新材料分配
    rho.vector()[:] *= 1.01
    for i in range(rho.vector().size()):
        if rho.vector()[i] > 1.0:
            rho.vector()[i] = 1.0

    # 解决方案
    plt.figure()
    plot(u_sol.sub(0), title="Displacement", mode="displacement")
    plt.colorbar()

    # 更新时间
    t += dt

# 绘制最终材料分布图
plt.figure()
plot(rho, title="Material distribution")
plt.colorbar()
plt.show()
```
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