% % Covariance_construct.m % % Code to construct a covariance function for the random field associated with the Helmholtz % energy % % \alpha(x,\omega) = a_1(\omega) x^2 + a_{11} x^4 + a_{111} x^6 % % clear all % % % Input polarization values and constants. Construct intervals with per=20% perturbation values. % Pf = 0.8; Nquad = 50; % Number of composite trapezoid or midpoint quadrature points Nmc = 50000; % Number of Monte Carlo sampling points method = 2; % method = 1: Composite trapezoid % method = 2; Composite midpoint % % if method==1 h = Pf/(Nquad - 1); x = 0:h:Pf; w = h*ones(1,Nquad); w(1) = 0.5*h; w(Nquad) = 0.5*h; else h = Pf/Nquad; w = h*ones(1,Nquad); for j=1:Nquad values(j) = 2*j-1; x(j) = (h/2)*values(j); end end a_1 = -382.4; % Mean value of a_1 a_11 = 760.3; % Mean value of a_11 a_111 = 155.5; % Mean value of a_111 per = 0.2; a1_sample = (a_1 - per*abs(a_1)) + 2*per*abs(a_1)*rand(Nmc,1); a11_sample = (a_11 - per*abs(a_11)) + 2*per*abs(a_11)*rand(Nmc,1); a111_sample = (a_111 - per*abs(a_111)) + 2*per*abs(a_111)*rand(Nmc,1); % % Plot the Helmholtz energy psi. % P = 0:.05:Pf; psi = a1_sample(1,1)*P.^2 + a11_sample(1,1)*P.^4 + a111_sample(1,1)*P.^6; figure(1) plot(P,psi,'b',P,0*P,'k','linewidth',3) set(gca,'Fontsize',[20]); xlabel('Polarization x') ylabel('Helmholtz Energy \alpha') %print -depsc Helmholtz % % Compute the matrices of random field evaluations and covariance matrix C as well as diagonal matrix W of quadrature weights % % Alpha: Matrix of random field evaluations -- alpha(x_j,omega^k) % Alpha_c: Matrix of centered process -- alpha_c(x_i,omega^k) = alpha(x_i,omega^k) - (1/Nmc)sum_{m=1}^Nmc alpha(x_i,omega^m) % C: Covariance matrix Alpha = a1_sample*(x.^2) + a11_sample*(x.^4) + a111_sample*(x.^6); Alpha_c = Alpha - (1/Nmc)*ones(Nmc,1)*sum(Alpha); C1 = (1/(Nmc-1))*(Alpha_c')*Alpha_c; % C1 can be slightly nonsymmetric due to numerical issues C = max(C1,C1.'); % This guarantees that C is symmetric W = diag(w); W2 = sqrtm(W); W2inv = inv(W2); A = W2*C*W2; % % Solve the symmetric eigenvalue problem and compute eigenvectors [V1,D1] = eig(A); D=diag(sort(diag(D1),'descend')); % The following commands sort D and V from largest to smallest [c, ind]=sort(diag(D),'descend'); V = V1(:,ind); Phi = W2inv*V; eigenvalues = sort(abs(diag(D)),'descend'); % % Compute the bound b_N given by (6.21) % b_N = sum(eigenvalues(4:30))/sum(eigenvalues(1:3)) % % Plot the normalized eigenvalues % figure(2) % semilogy(eigenvalues/eigenvalues(1),'x','linewidth',3); semilogy(eigenvalues(1:30),'b+','linewidth',3); axis([1 30 1e-16 2000]) set(gca,'Fontsize',[20]); xlabel('n') % ylabel('\lambda_n/\lambda_1') ylabel('\lambda_n') %print -depsc eigs_Helmholtz % % Construct the first three KL modes Q_n(\omega) % clear Alpha Alpha_c omega_vals = 0:.01:1; N_omega = length(omega_vals); a1_s = (a_1 - per*abs(a_1)) + 2*per*abs(a_1)*omega_vals'; a11_s = (a_11 - per*abs(a_11)) + 2*per*abs(a_11)*omega_vals'; a111_s = (a_111 - per*abs(a_111)) + 2*per*abs(a_111)*omega_vals'; Alpha = a1_s*(x.^2) + a11_s*(x.^4) + a111_s*(x.^6); Alpha_c = Alpha - (1/N_omega)*ones(N_omega,1)*sum(Alpha); for k=1:N_omega sum1 = 0; sum2 = 0; sum3 = 0; for j=1:Nquad sum1 = sum1 + w(j)*Alpha_c(k,j)*Phi(j,1); sum2 = sum2 + w(j)*Alpha_c(k,j)*Phi(j,2); sum3 = sum3 + w(j)*Alpha_c(k,j)*Phi(j,3); end Qn(k,1) = (1/sqrt(eigenvalues(1)))*sum1; Qn(k,2) = (1/sqrt(eigenvalues(2)))*sum2; Qn(k,3) = (1/sqrt(eigenvalues(3)))*sum3; end figure(3) plot(Qn(:,1)) % % Compute and plot the covariance function c(x,y) % x_small = x(1:2:end); C_small = C(1:2:end,1:2:end); figure(3) mesh(x_small,x_small,C_small,'linewidth',2) brighten(-.2) set(gca,'Fontsize',[20]); xlabel('x-axis') ylabel('y-axis') zlabel('c(x,y)') % %