#!/usr/bin/env python # coding: utf-8 # # Rates of Convergence # # Copyright (C) 2010-2020 Luke Olson
# Copyright (C) 2020 Andreas Kloeckner # #
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# In[1]: from firedrake import * import numpy as np import matplotlib.pyplot as plt # ## A Boundary Value Problem # # Consider # $$u_{*} = \sin(\omega \pi x) \sin(\omega \pi y)$$ # on the unit square with $\omega = 2$ as a start. # In[2]: omega = 2 # ## Refine the Mesh and Check the Error # In[12]: errsH0 = [] errsH1 = [] hs = [] for nx in [4, 8, 16, 32, 64, 128]: # , 256, 512]: print(f"Now solving {nx}x{nx}...") mesh = UnitSquareMesh(nx, nx) V = FunctionSpace(mesh, "Lagrange", 1) Vexact = FunctionSpace(mesh, "Lagrange", 7) x = SpatialCoordinate(V.mesh()) u_exact = interpolate(sin(omega*pi*x[0])*sin(omega*pi*x[1]), Vexact) f = 2*pi**2*omega**2*u_exact # all four sides of the square bc = DirichletBC(V, 0.0, [1, 2, 3, 4]) u = TrialFunction(V) v = TestFunction(V) a = inner(grad(u), grad(v))*dx L = f*v*dx u = Function(V) solve(a == L, u, bc) EH0 = errornorm(u_exact, u, norm_type='L2') EH1 = errornorm(u_exact, u, norm_type='H1') errsH0.append(EH0) errsH1.append(EH1) hs.append(1/nx) # In[13]: errsH0 = np.array(errsH0) errsH1 = np.array(errsH1) hs = np.array(hs) rH0 = np.log(errsH0[1:] / errsH0[0:-1]) / np.log(hs[1:] / hs[0:-1]) rH1 = np.log(errsH1[1:] / errsH1[0:-1]) / np.log(hs[1:] / hs[0:-1]) print(rH0) print(rH1) # In[15]: plt.loglog(hs, errsH0, "o-", label="$H^0$ error") plt.loglog(hs, errsH1, "o-", label="$H^1$ error") plt.loglog(hs, hs**1, "o-", label="$h^1$") plt.loglog(hs, hs**2, "o-", label="$h^2$") plt.legend() # - Now play with $\omega$ and the element order. # In[ ]: