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Commit 9979cff5 authored by Davide Torlo's avatar Davide Torlo
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julia all the code of the paper, python still missing a notebook.

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Notebooks/julia/DeC.jl 0 → 100644
View file @ 9979cff5
#using Plots
using FastGaussQuadrature, Compat
using LinearAlgebra
using Polynomials
"""Different models"""
function lagrange_basis(X,t,i)
idxs = eachindex(X)
if t in X
if t == X[i]
return 1
else
return 0
end
end
return prod((t-X[j])/(X[i]-X[j]) for j in idxs if j != i)
end
function equispaced(order)
nodes=range(-1,stop=1,length=order)
w=zeros(order)
for k=1:order
zz=Polynomial([1.])
for s=1:order
if s == k
continue
else
zz=zz* Polynomial([-nodes[s],1])
end
end
zz=zz/zz(nodes[k])
intzz=integrate(zz)
w[k]=intzz(1)-intzz(-1)
end
return nodes, w
end
function get_nodes(order,nodes_type)
if nodes_type=="equispaced"
nodes, w = equispaced(order)
elseif nodes_type=="gaussLobatto"
nodes, w = gausslobatto(order)
elseif nodes_type=="gaussLegendre"
nodes, w = gausslegendre(order)
end
w = map( x-> x*0.5, w)
nodes = nodes*0.5.+0.5
return nodes, w
end
function compute_theta_DeC(order, nodes_type)
nodes, w = get_nodes(order,nodes_type)
int_nodes, int_w = get_nodes(order,"gaussLobatto")
# generate theta coefficients
theta = zeros(order,order)
beta_m =zeros(order)
for m in 1:order
beta_m[m] =nodes[m]
nodes_m = int_nodes*(nodes[m])
w_m = int_w*(nodes[m])
for r in 1:order
theta[r,m] = sum([lagrange_basis(nodes,nodes_m[k],r)*w_m[k] for k in 1:order])
end
end
return theta, beta_m
end
function compute_theta_DeC_subtimesteps(order, nodes_type)
nodes, w = get_nodes(order,nodes_type)
int_nodes, int_w = get_nodes(order,"gaussLobatto")
# generate theta coefficients
theta = zeros(order,order)
beta_m =zeros(order)
for m in 1:order
if m==1
beta_m[m] = nodes[1]
nodes_m=int_nodes*beta_m[m]
w_m = int_w*beta_m[m]
else
beta_m[m] = (nodes[m]-nodes[m-1])
nodes_m = int_nodes*beta_m[m] .+nodes[m-1]
w_m = int_w*beta_m[m]
end
for r in 1:order
theta[r,m] = sum([lagrange_basis(nodes,nodes_m[k],r)*w_m[k] for k in 1:order])
end
end
return theta, beta_m
end
""" Computing the matrix of the values T_k^m = int_{t^0}^{t^m} varphi_k"""
function calculate_theta(M_sub::Int, distribution)
if distribution=="equispaced"
nodes=range(0,stop=1, length=M_sub+1)
elseif distribution=="gaussLobatto"
nodes,p = gausslobatto(M_sub+1)
nodes=nodes*0.5.+0.5
end
theta=compute_theta(nodes)
return theta
end
#Dec_correction-
#Func is the function of the ode
#N_step -> Number of subtimesteps
#Corr-> Number of Corrections
#y_0 Initial condition
# t_0 Start value
# t_end End value
# Maybe logical variable for checking for the different solvers or the distributaiton
# of subtimesteps up to some order
#Distribution is set equidistand or different order
""" Dec_correction constant time step"""
function dec(func, tspan, y_0, M_sub::Int, K_corr::Int, distribution)
N_time=length(tspan)
dim=length(y_0)
U=zeros(dim, N_time)
u_p=zeros(dim, M_sub+1)
u_a=zeros(dim, M_sub+1)
u_help=zeros(dim)
func_corr=zeros(dim,M_sub+1)
Theta, beta = compute_theta_DeC(M_sub+1,distribution)
U[:,1]=y_0
for it=2: N_time
delta_t=(tspan[it]-tspan[it-1])
for m=1:M_sub+1
u_a[:,m]=U[:,it-1]
u_p[:,m]=U[:,it-1]
end
for k=2:K_corr+1
u_p=copy(u_a)
for r=1:M_sub+1
func_corr[:,r]=func(u_p[:,r])
end
for m=2:M_sub+1
u_a[:,m]= u_a[:,1]+delta_t*sum(Theta[r,m]*func_corr[:,r] for r in 1:M_sub+1)
end
end
U[:,it]=u_a[:,M_sub+1]
end
return tspan, U
end
function dec_subtimesteps(func, tspan, y_0, M_sub::Int, K_corr::Int, distribution)
N_time=length(tspan)
dim=length(y_0)
U=zeros(dim, N_time)
u_p=zeros(dim, M_sub+1)
u_a=zeros(dim, M_sub+1)
u_help=zeros(dim)
func_corr=zeros(dim,M_sub+1)
Theta, beta = compute_theta_DeC_subtimesteps(M_sub+1,distribution)
U[:,1]=y_0
for it=2: N_time
delta_t=(tspan[it]-tspan[it-1])
for m=1:M_sub+1
u_a[:,m]=U[:,it-1]
u_p[:,m]=U[:,it-1]
end
for k=2:K_corr+1
u_p=copy(u_a)
for r=1:M_sub+1
func_corr[:,r]=func(u_p[:,r])
end
for m=2:M_sub+1
u_a[:,m]= u_a[:,m-1]+delta_t*sum(Theta[r,m]*func_corr[:,r] for r in 1:M_sub+1)
end
end
U[:,it]=u_a[:,M_sub+1]
end
return tspan, U
end
""" Dec_correction constant time step"""
function energy_relaxed_dec(func, t0, t_end, dt0, y_0, M_sub::Int, K_corr::Int, distribution)
dim=length(y_0)
U=zeros(dim,1)
U[:,1]=y_0
time=t0
tSpan=t0
gammas=1.0
it=1
u_p=zeros(dim, M_sub+1)
u_a=zeros(dim, M_sub+1)
u_help=zeros(dim)
func_corr=zeros(dim,M_sub+1)
Theta, beta = compute_theta_DeC(M_sub+1,distribution)
while (time <t_end)
it=it+1
delta_t=dt0
for m=1:M_sub+1
u_a[:,m]=U[:,it-1]
u_p[:,m]=U[:,it-1]
end
for k=2:K_corr+1
u_p=copy(u_a)
for r=1:M_sub+1
func_corr[:,r]=func(u_p[:,r])
end
for m=2:M_sub+1
u_a[:,m]= u_a[:,1]+delta_t*sum(Theta[r,m]*func_corr[:,r] for r in 1:M_sub+1)
end
end
udiff=u_a[:,M_sub+1]-U[:,it-1]
gamma=-2*(udiff'*U[:,it-1])/(udiff'*udiff)
delta_t_relaxed=gamma*delta_t
time= time+delta_t_relaxed
tSpan=[tSpan time]
gammas=[gammas gamma]
uRelaxed = U[:,it-1]+gamma*udiff
U=[U uRelaxed]
end
return tSpan, U, gammas
end
""" Dec_correction constant time step"""
function energy_relaxed_dec_dissipative(func, t0, t_end, dt0, y_0, M_sub::Int, K_corr::Int, distribution)
dim=length(y_0)
U=zeros(dim,1)
U[:,1]=y_0
time=t0
tSpan=t0
gammas=1.0
it=1
u_p=zeros(dim, M_sub+1)
u_a=zeros(dim, M_sub+1)
u_help=zeros(dim)
func_corr=zeros(dim,M_sub+1)
Theta, beta = compute_theta_DeC(M_sub+1,distribution)
while (time <t_end)
it=it+1
delta_t=dt0
for m=1:M_sub+1
u_a[:,m]=U[:,it-1]
u_p[:,m]=U[:,it-1]
end
for k=2:K_corr+1
u_p=copy(u_a)
for r=1:M_sub+1
func_corr[:,r]=func(u_p[:,r])
end
for m=2:M_sub+1
u_a[:,m]= u_a[:,1]+delta_t*sum(Theta[r,m]*func_corr[:,r] for r in 1:M_sub+1)
end
end
udiff=u_a[:,M_sub+1]-U[:,it-1]
gamma=2*delta_t*sum(Theta[r,M_sub+1]*func_corr[:,r]'*(u_p[:,r]-U[:,it-1]) for r in 1:M_sub+1)/(udiff'*udiff)
delta_t_relaxed=gamma*delta_t
time= time+delta_t_relaxed
tSpan=[tSpan time]
gammas=[gammas gamma]
uRelaxed = U[:,it-1]+gamma*udiff
U=[U uRelaxed]
end
return tSpan, U, gammas
end
function dec_stiff(func, jac_stiff, tspan, y_0, M_sub::Int, K_corr::Int, distribution)
verbose=0
N_time=length(tspan)
dim=length(y_0)
U=zeros(dim, N_time)
u_p=zeros(dim, M_sub+1)
u_a=zeros(dim, M_sub+1)
u_help=zeros(dim)
func_corr=zeros(dim,M_sub+1)
Theta, beta = compute_theta_DeC(M_sub+1,distribution)
invJac=zeros(M_sub+1,dim,dim)
U[:,1]=y_0
for it=2: N_time
delta_t=(tspan[it]-tspan[it-1])
for m=1:M_sub+1
u_a[:,m]=U[:,it-1]
u_p[:,m]=U[:,it-1]
end
SS=jac_stiff(u_p[:,1])
for m=2:M_sub+1
invJac[m,:,:]=inv(Matrix(I,dim,dim).+ delta_t*beta[m] .*SS)
end
for k=2:K_corr+1
u_p=copy(u_a)
for r=1:M_sub+1
func_corr[:,r]=func(u_p[:,r])
end
for m=2:M_sub+1
u_a[:,m]= u_p[:,m]+delta_t.*invJac[m,:,:]*(-(u_p[:,m]-u_p[:,1])./delta_t+sum(Theta[r,m]*func_corr[:,r] for r in 1:M_sub+1))
end
if verbose==1
println("dt $(delta_t)")
println("up $(u_p)")
println("jac $(jac_stiff(u_p[:,1]))")
println("invJac $(invJac)")
end
end
U[:,it]=u_a[:,M_sub+1]
end
return tspan, U
end
function dec_stiff_subtimesteps(func, jac_stiff, tspan, y_0, M_sub::Int, K_corr::Int, distribution)
verbose=0
N_time=length(tspan)
dim=length(y_0)
U=zeros(dim, N_time)
u_p=zeros(dim, M_sub+1)
u_a=zeros(dim, M_sub+1)
u_help=zeros(dim)
func_corr=zeros(dim,M_sub+1)
Theta, beta = compute_theta_DeC_subtimesteps(M_sub+1,distribution)
S = zeros(dim,dim)
invJac=zeros(M_sub+1,dim,dim)
U[:,1]=y_0
for it=2: N_time
delta_t=(tspan[it]-tspan[it-1])
for m=1:M_sub+1
u_a[:,m]=U[:,it-1]
u_p[:,m]=U[:,it-1]
end
SS=jac_stiff(u_p[:,1])
for m=1:M_sub+1
invJac[m,:,:]=inv(Matrix(I,dim,dim) .+ delta_t*beta[m] .*SS)
end
for k=2:K_corr+1
u_p=copy(u_a)
for r=1:M_sub+1
func_corr[:,r]=func(u_p[:,r])
end
for m=2:M_sub+1
L2=(u_p[:,m]-u_a[:,m-1])-delta_t.*sum(Theta[r,m]*func_corr[:,r] for r in 1:M_sub+1) #
u_a[:,m]= u_p[:,m]+ invJac[m,:,:]*( - L2) #u_a[:,m-1]-u_p[:,m-1]
#u_a[:,m]= invJac[m,:,:]*(u_a[:,m-1] +delta_t*beta[m] .*SS*u_p[:,m] -L2)
end
if verbose==1
println("beta $(beta)")
println("theta $(Theta)")
println("dt $(delta_t)")
println("up $(u_p)")
println("jac $(jac_stiff(u_p[:,1]))")
println("invJac $(invJac)")
end
end
U[:,it]=u_a[:,M_sub+1]
end
return tspan, U
end
Notebooks/julia/Lagrange_polynomials.jl 0 → 100644
View file @ 9979cff5
using SymPy
import SymPy
function generate_poly(s, nodes,deg)
#Generates Lagrangian polynomial in symbolic
aux=[]
phi=[]
for k=1:deg+1
append!(aux, 1);
for j=1:deg+1
if j != k
aux[k]=aux[k].*(s-nodes[j]);
end
end
end
for j=1:deg+1
aux[j]= aux[j]/SymPy.N(subs(aux[j],s,nodes[j]));
end
return aux
end
function compute_theta(nodes)
# Compute the integral of lagrange polynomials from 0 to interpolation point theta[r,m]= \int_{t^0}^{t^m} \phi_r
s=Sym("s");
deg=length(nodes)-1;
poly=generate_poly(s,nodes,deg)
theta=zeros(deg+1, deg+1)
for r=1:deg+1
for m=1:deg+1
theta[r,m]=SymPy.integrate(poly[r],(s,0,nodes[m]))
end
end
return theta
end
Notebooks/julia/RelaxationDeC.ipynb 0 → 100644
View file @ 9979cff5
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Notebooks/python/DeC.py 0 → 100644
View file @ 9979cff5
import numpy as np
import matplotlib.pyplot as plt
from numpy.polynomial.legendre import leggauss
from quadr import lglnodes,equispaced
def lagrange_basis(nodes,x,k):
y=np.zeros(x.size)
for ix, xi in enumerate(x):
tmp=[(xi-nodes[j])/(nodes[k]-nodes[j]) for j in range(len(nodes)) if j!=k]
y[ix]=np.prod(tmp)
return y
def get_nodes(order,nodes_type):
if nodes_type=="equispaced":
nodes,w = equispaced(order)
elif nodes_type == "gaussLegendre":
nodes,w = leggauss(order)
elif nodes_type == "gaussLobatto":
nodes, w = lglnodes(order-1,10**-15)
nodes=nodes*0.5+0.5
w = w*0.5
return nodes, w
def compute_theta_DeC(order, nodes_type):
nodes, w = get_nodes(order,nodes_type)
int_nodes, int_w = get_nodes(order,"gaussLobatto")
# generate theta coefficients
theta = np.zeros((order,order))
beta = np.zeros(order)
for m in range(order):
beta[m] = nodes[m]
nodes_m = int_nodes*(nodes[m])
w_m = int_w*(nodes[m])
for r in range(order):
theta[r,m] = sum(lagrange_basis(nodes,nodes_m,r)*w_m)
return theta, beta
def compute_RK_from_DeC(M_sub,K_corr,nodes_type):
order=M_sub+1;
[theta,beta]=compute_theta_DeC(order,nodes_type)
bar_beta=beta[1:] # M_sub
bar_theta=theta[:,1:].transpose() # M_sub x (M_sub +1)
theta0= bar_theta[:,0] # M_sub x 1
bar_theta= bar_theta[:,1:] #M_sub x M_sub
A=np.zeros((M_sub*(K_corr-1)+1,M_sub*(K_corr-1)+1)) # (M_sub x K_corr +1)^2
b=np.zeros(M_sub*(K_corr-1)+1)
c=np.zeros(M_sub*(K_corr-1)+1)
c[1:M_sub+1]=bar_beta
A[1:M_sub+1,0]=bar_beta
for k in range(1,K_corr-1):
r0=1+M_sub*k
r1=1+M_sub*(k+1)
c0=1+M_sub*(k-1)
c1=1+M_sub*(k)
c[r0:r1]=bar_beta
A[r0:r1,0]=theta0
A[r0:r1,c0:c1]=bar_theta
b[0]=theta0[-1]
b[-M_sub:]=bar_theta[M_sub-1,:]
return A,b,c
def dec(func, tspan, y_0, M_sub, K_corr, distribution):
N_time=len(tspan)
dim=len(y_0)
U=np.zeros((dim, N_time))
u_p=np.zeros((dim, M_sub+1))
u_a=np.zeros((dim, M_sub+1))
rhs= np.zeros((dim,M_sub+1))
Theta, beta = compute_theta_DeC(M_sub+1,distribution)
U[:,0]=y_0
for it in range(1, N_time):
delta_t=(tspan[it]-tspan[it-1])
for m in range(M_sub+1):
u_a[:,m]=U[:,it-1]
u_p[:,m]=U[:,it-1]
for k in range(1,K_corr+1):
u_p=np.copy(u_a)
for r in range(M_sub+1):
rhs[:,r]=func(u_p[:,r])
for m in range(1,M_sub+1):
u_a[:,m]= U[:,it-1]+delta_t*sum([Theta[r,m]*rhs[:,r] for r in range(M_sub+1)])
U[:,it]=u_a[:,M_sub]
return tspan, U
def decImplicit(func,jac_stiff, tspan, y_0, M_sub, K_corr, distribution):
N_time=len(tspan)
dim=len(y_0)
U=np.zeros((dim, N_time))
u_p=np.zeros((dim, M_sub+1))
u_a=np.zeros((dim, M_sub+1))
u_help= np.zeros(dim)
rhs= np.zeros((dim,M_sub+1))
Theta, beta = compute_theta_DeC(M_sub+1,distribution)
invJac=np.zeros((M_sub+1,dim,dim))
U[:,0]=y_0
for it in range(1, N_time):
delta_t=(tspan[it]-tspan[it-1])
for m in range(M_sub+1):
u_a[:,m]=U[:,it-1]
u_p[:,m]=U[:,it-1]
SS=jac_stiff(u_p[:,0])
for m in range(1,M_sub+1):
invJac[m,:,:]=np.linalg.inv(np.eye(dim) - delta_t*beta[m]*SS)
for k in range(1,K_corr+1):
u_p=np.copy(u_a)
for r in range(M_sub+1):
rhs[:,r]=func(u_p[:,r])
for m in range(1,M_sub+1):
u_a[:,m]= u_p[:,m]+delta_t*np.matmul(invJac[m,:,:],\
(-(u_p[:,m]-u_p[:,0])/delta_t\
+sum([Theta[r,m]*rhs[:,r] for r in range(M_sub+1)])))
U[:,it]=u_a[:,M_sub]
return tspan, U
def decMPatankar(prod_dest, rhs, tspan, y_0, M_sub, K_corr, distribution):
N_time=len(tspan)
dim=len(y_0)
U=np.zeros((dim, N_time))
u_p=np.zeros((dim, M_sub+1))
u_a=np.zeros((dim, M_sub+1))
prod_p = np.zeros((dim,dim,M_sub+1))
dest_p = np.zeros((dim,dim,M_sub+1))
rhs_p= np.zeros((dim,M_sub+1))
Theta, beta = compute_theta_DeC(M_sub+1,distribution)
U[:,0]=y_0
for it in range(1, N_time):
delta_t=(tspan[it]-tspan[it-1])
for m in range(M_sub+1):
u_a[:,m]=U[:,it-1]
u_p[:,m]=U[:,it-1]
for k in range(1,K_corr+1):
u_p=np.copy(u_a)
for r in range(M_sub+1):
prod_p[:,:,r], dest_p[:,:,r]=prod_dest(u_p[:,r])
rhs_p[:,r]=rhs(u_p[:,r])
for m in range(1,M_sub+1):
u_a[:,m]= patankar_type_dec(prod_p,dest_p,rhs_p,delta_t,m,M_sub,Theta,u_p,dim)
U[:,it]=u_a[:,M_sub]
return tspan, U
def patankar_type_dec(prod_p,dest_p,rhs_p,delta_t,m,M_sub,Theta,u_p,dim):
mass= np.eye(dim)
RHS= u_p[:,0]
for i in range(dim):
for r in range(M_sub+1):
RHS[i]=RHS[i]+delta_t*Theta[r,m]*rhs_p[i,r]
if Theta[r,m]>0:
for j in range(dim):
mass[i,j]=mass[i,j]-delta_t*Theta[r,m]*(prod_p[i,j,r]/u_p[j,m])
mass[i,i]=mass[i,i]+ delta_t*Theta[r,m]*(dest_p[i,j,r]/u_p[i,m])
elif Theta[r,m]<0:
for j in range(dim):
mass[i,i]=mass[i,i]- delta_t*Theta[r,m]*(prod_p[i,j,r]/u_p[i,m])
mass[i,j]=mass[i,j]+ delta_t*Theta[r,m]*(dest_p[i,j,r]/u_p[j,m])
return np.linalg.solve(mass,RHS)
Notebooks/python/ODEproblems.py 0 → 100644
View file @ 9979cff5
import numpy as np
## Linear scalar Dahlquist's equation
def linear_scalar_flux(u,t=0,k_coef=10):
ff=np.zeros(np.shape(u))
ff[0]= -k_coef*u[0]
return ff
def linear_scalar_exact_solution(u0,t,k_coef=10):
return np.array([np.exp(-k_coef*u0[0]*t)])