THE PBDW method

import sys
!{sys.executable} -m pip install numpy
!{sys.executable} -m pip install matplotlib
!{sys.executable} -m pip install scikit-fem

Let us present one method that combines model order reduction and a data assimilation problem: the Parametrized-Background Data-Weak method (PBDW).

$$ G^{bk,\mu}(u^{bk}(\mu)) = 0.$$$$ \mathcal{M}^{bk} = \{u^{bk}(\mu) : \mu \in \mathcal{P}^{bk}\}.$$

The PBDW formulation integrates the parameterized mathematical model $G^{pb}$ and $M$ experimental observations associated with a parameter configuration $\mu^{true}$ to estimate the true field $u^{true}(\mu^{true})$ as well as any desired output $l_{out}(u^{true}(\mu^{true}\ )) \in \mathbb{R}$ for given output functional $l_{out}$.
We intend that $\lVert u^{true}(\mu^{true})-u^{bk}(\mu^{true}) \rVert$ is small (i.e. that our model represents our data observations well).

The PBDW is decomposed in two parts: one offline and one online.

In the following notebook, we will employ:

as our model problem a 2D advection-diffusion problem,
some measures that could also be possibly be noisy (here we will consider the case with no noise),
a sequence of background spaces that reflect our (prior) best knowledge model bk,
gaussians for our observations with a proper choice of localization.

We will create measures artificially from our model problem by modifying the right hand side of our equation and the boundary conditions.

$$\mathcal{Z}_1 \subset \dots \subset \mathcal{Z}_{N} \subset \mathcal{U}. $$

To sum up, we consider two main ingredients:

a model with a bias leading to the $(\mathcal{Z}_n)_n$ sequence,
and true measures, that might be noisy.

# import packages
import skfem  # for Finite Element Method
import numpy as np
import matplotlib.pyplot as plt

The 2D advection-diffusion model problem:

We are going to use in this example a 2D advection-diffusion problem with the Finite Element Method (FEM), which consists in solving on a unit square (denoted $\Omega$) the following equations: \begin{align} &- \Delta u + b(\mu) \cdot \nabla u =x_1 x_2 + g_1, \textrm{ in } \Omega,\ & u=4 x_2(1-x_2)(1+g_2), \textrm{ on } \Omega_{left}:=\partial \Omega \cap {x=0},\ & \partial_n u (\mu)=0, \textrm{ on } \partial \Omega \backslash \Omega_{left}, \end{align} where $u \in H^1(\Omega)$ represents the unknown, $\mu \in \mathbb{R}^2$ is our variable parameter, and $g_1$ and $g_2$ are two functions that allow us to construct a model with a bias with respect to the data observations. The lack of knowledge of the value of $\mu$ constitutes the anticipated parametric ignorance in the model, while uncertainty in $g_1$ and $g_2$ constitutes the unanticipated non-parametric ignorance.

We employ $P1$ finite elements to get a proper solution and obtain the system $\mathbf{A} \mathbf{x} =\mathbf{f}$ to solve where the assembled matrix $\mathbf{A}$ corresponds to the bilinear part $\int_{\Omega} (\nabla u, \nabla v) - (b(\mu),\nabla v) u + \int_{\Omega_{left}} (b(\mu), \nabla_n u) v $.

The dirichlet boundary conditions are imposed with a penalization method, called by the line: solve(*condense(K,f, x=uvp, D=D)),

where x=uvp gives the values at the boundaries of the velocity and D refers to the boundary decomposition.

This problem model comes from the paper “PBDW method for state estimation: error analysis for noisy data and non-linear formulation”(https://arxiv.org/abs/1906.00810).

# First we define a mesh for the unit square
mesh= skfem.MeshTri().refined(5).with_boundaries(                                                                
        {                                                                                                                                
            "left": lambda x: x[0] == 0,                                                                                            
            "right": lambda x: x[0] == 1,            
            "down": lambda x: x[1] == 0,                                                                                            
            "up": lambda x: x[1] == 1,     
            
        }                                                                                                                               
)

print(mesh)
mesh

<skfem MeshTri1 object>
  Number of elements: 2048
  Number of vertices: 1089
  Number of nodes: 1089
  Named boundaries [# facets]: left [32], bottom [32], right [32], top [32], down [32], up [32]

"""First step."""

# Assembling matrices

from skfem.assembly import BilinearForm, LinearForm
from skfem.helpers import grad, dot

# Bilinear form 
@BilinearForm
def laplace(u, v, _):
    return dot(grad(u), grad(v))

@BilinearForm
def gradu(u, v, w):    
    return - u*(v.grad[0]*w.bmu1+v.grad[1]*w.bmu2) 

@BilinearForm
def boundary_normal_gradient(u, v, w):
    """ Bilinear part for int(grad_n(u), v) on the left boundary """
    return dot(grad(u), w.n) * v

@BilinearForm
def mass(u, v, _): #H1 norm (norm for the space U)
    return u * v +dot(grad(u), grad(v))

@BilinearForm
def L2mass(u, v, _): #L2 norm
    return u * v

In the code bellow, the function SolveAdvDiffProblem([mu1,mu2],Mesh,measures) takes as inputs the parameter $\mu \in \mathcal{P}^{bk} \subset \mathbb{R}^2$, a mesh, and a boolean used for the bias on the right-hand side function and on the dirichlet boundary conditions, and it returns the associated solution.

from skfem import *
# Compute solution 
element = ElementTriP1() #P1 FEM elements

def SolveAdvDiffProblem(mu,Mesh,measures):
    # mu: variable parameter
    # mesh: FE mesh
    # measures: Boolean (if true, the right-hand side function and dirichlet parameter are adapted to the generation of the data observations)
    basis = Basis(Mesh, element) 
    #print('Parameter mu:',mu)
    mu1,mu2=mu[0],mu[1]
    bmu1= lambda x: mu[0]*(np.cos(mu[1])) 
    bmu2= lambda x: mu[0]*(np.sin(mu[1])) 
    bmu1=basis.project(bmu1)
    bmu2=basis.project(bmu2)
    
    ## Assembling global problem
    A= laplace.assemble(basis)
    A2=gradu.assemble(basis,bmu1=basis.interpolate(bmu1),bmu2=basis.interpolate(bmu2))
    A+=A2

    ## boundary conditions
    left_basis = FacetBasis(Mesh, element, facets=Mesh.boundaries['right'])
    
    def profil_left(x):
        # Dirichlet boundary conditions
        if measures==True:
            #return (x[0]==1)*200*x[1]#(8*x[1]*(1-x[1])*(1+ np.sin(2*np.pi*x[1]))) #with g2!=0
            return (x[0]==1)*(4*x[1]*(1-x[1])*(1+ np.sin(2*np.pi*x[1]))) #with g2!=0
        else:
            #return (x[0]==1)*100*x[1]#(4*x[1]*(1-x[1]))  #with g2=0
            return (x[0]==1)*(4*x[1]*(1-x[1]))  #with g2=0

    u_boundary=left_basis.project(profil_left)
    D = basis.get_dofs(['right'])
    ## Neumann boundary conditions 
    A_boundary = boundary_normal_gradient.assemble(left_basis)
    A+=A_boundary

    ## Right hand side functions
    if measures==True:
        #g1 = lambda x: x[0]**2 -x[1] #with g1!=0
        g1 = lambda x: 0.2*x[0]**2 +x[0]*x[1] #with g1!=0
        g_proj1 = basis.project(g1)
    else:
        #g1 = lambda x: -x[1] #with g1=0
        g1 = lambda x: x[0]*x[1] #with g1!=0
        g_proj1 = basis.project(g1)
        
    f=g_proj1
    u = solve(*condense(A,f, x=u_boundary, D=D))

    return u

## plot the solution

from skfem.visuals.matplotlib import plot, draw, savefig

u=SolveAdvDiffProblem([0.4,np.pi/6],mesh,True) #
plot(mesh, u, shading='gouraud', colorbar=True)

u=SolveAdvDiffProblem([0.4,np.pi/6],mesh,False) # examples
plot(mesh, u, shading='gouraud', colorbar=True)

<Axes: >

The PBDW method

We are now able to proceed with the offline and online parts of the PBDW method. We start with a classical POD algorithm on the bk model with bias, but first we construct the data observations $u^{true}(\mu^{true})$ artificially (here we consider that we have access to the data in the whole domain).

OFFLINE PART

We define one fine mesh called “FineMesh” in the code

## FINE MESH
FineMesh = skfem.MeshTri().refined(5).with_boundaries(                                                                
        {                                                                                                                                
            "left": lambda x: x[0] == 0,                                                                                            
            "right": lambda x: x[0] == 1,            
            "down": lambda x: x[1] == 0,                                                                                            
            "up": lambda x: x[1] == 1,     
            
        }                                                                                                                               
)
FineBasis = Basis(FineMesh, element)

NumberOfNodesFineMesh = FineMesh.p.shape[1]
print("number of nodes: ",NumberOfNodesFineMesh)
num_dofs_uFineMesh = FineBasis.doflocs.shape[1] # or np.shape(u)[0] for DOFs
print("number of DoFs: ",num_dofs_uFineMesh)

massMat=mass.assemble(FineBasis)
L2massMat=L2mass.assemble(FineBasis)

number of nodes:  1089
number of DoFs:  1089

utrue=SolveAdvDiffProblem([1.4,np.pi/3],mesh,True) # True for measures
plot(FineMesh, utrue, shading='gouraud', colorbar=True)

<Axes: >

POD on bk model

""" POD on the bk model (could be done with a greedy approach) """
print("-----------------------------------")
print("        Offline POD                ")
print("-----------------------------------")

NumberOfSnapshots=0
NumberOfModesN=15
print("number of modes for the bk model: ",NumberOfModesN)

mu1list=np.linspace(0.1,3,10)
mu2list=np.linspace(0,np.pi/2,10) 

FineSnapshots=[]

for mu1 in (mu1list):
    for mu2 in (mu2list):
        u=SolveAdvDiffProblem([mu1,mu2],FineMesh,False)
        FineSnapshots.append(u)
        NumberOfSnapshots+=1

## SVD ##
 
# We first compute the correlation matrix C_ij = (u_i,u_j)_U
CorrelationMatrix = np.zeros((NumberOfSnapshots, NumberOfSnapshots))
for i, snapshot1 in enumerate(FineSnapshots):
    MatVecProduct = massMat.dot(snapshot1)
    for j, snapshot2 in enumerate(FineSnapshots):
        if i >= j:
            CorrelationMatrix[i, j] = np.dot(MatVecProduct, snapshot2)
            CorrelationMatrix[j, i] = CorrelationMatrix[i, j]

# Then, we compute the eigenvalues/eigenvectors of C 
EigenValues, EigenVectors = np.linalg.eigh(CorrelationMatrix, UPLO="L") #SVD: C eigenVectors=eigenValues eigenVectors
idx = EigenValues.argsort()[::-1] # sort the eigenvalues

TotEigenValues = EigenValues[idx]
TotEigenVectors = EigenVectors[:, idx]
EigenValues=TotEigenValues[0:NumberOfModesN]

EigenVectors=TotEigenVectors[:,0:NumberOfModesN]
print("eigenvalues: ",EigenValues)

RIC=1-np.sum(EigenValues)/np.sum(TotEigenValues) #must be close to 0
print("Relativ Information Content:",RIC)

ChangeOfBasisMatrix = np.zeros((NumberOfModesN,NumberOfSnapshots))

for j in range(NumberOfModesN):#orthonormalization
    ChangeOfBasisMatrix[j,:] = EigenVectors[:,j]/np.sqrt(EigenValues[j])

bkReducedBasis = np.dot(ChangeOfBasisMatrix,FineSnapshots)

-----------------------------------
        Offline POD                
-----------------------------------
number of modes for the bk model:  15
eigenvalues:  [2.03866121e+06 1.98788941e+05 7.63460590e+04 3.57206711e+03
 3.26553742e+03 1.08125000e+03 4.08015648e+01 3.47784483e+01
 2.37761217e+01 4.40644071e+00 3.45055983e-01 1.56321411e-01
 1.29748847e-01 6.77589026e-02 9.30473722e-03]
Relativ Information Content: 1.3066825399477011e-09

Now we can check for the reduced basis accuracy.

print("-----------------------------------")
print("             Reduced basis accuracy")
print("-----------------------------------")
### Offline Errors
print("Offline Errors")
CompressionErrors=[]
H1compressionErrors=[]

#for snap in FineSnapshots:
#    ExactSolution =snap
for mu1 in (mu1list):
    for mu2 in (mu2list):
        ExactSolution=SolveAdvDiffProblem([mu1,mu2],FineMesh,False)
        #ExactSolutionbis=SolveAdvDiffProblem([mu1,mu2],FineMesh,False)
        CompressedSolutionU= ExactSolution@(massMat@bkReducedBasis.transpose())
        ReconstructedCompressedSolution = np.dot(CompressedSolutionU, bkReducedBasis) #pas de tps 0
    
        norml2ExactSolution=np.sqrt(ExactSolution@(L2massMat@ExactSolution))
        normh1ExactSolution=np.sqrt(ExactSolution@(massMat@ExactSolution))
        t=ReconstructedCompressedSolution-ExactSolution
    
        if norml2ExactSolution !=0 and normh1ExactSolution != 0:
            relError=np.sqrt(t@L2massMat@t)/norml2ExactSolution
            relh1Error=np.sqrt(t@massMat@t)/normh1ExactSolution
        else:
            relError = np.linalg.norm(ReconstructedCompressedSolution-ExactSolution)
        CompressionErrors.append(relError)
        H1compressionErrors.append(relh1Error)
        if mu1==2.275 and mu2==0:
            plot(FineMesh, ExactSolution, shading='gouraud', colorbar=True)
            plot(FineMesh, ExactSolutionbis, shading='gouraud', colorbar=True)
            plot(FineMesh, ReconstructedCompressedSolution, shading='gouraud', colorbar=True)
print("L2 compression error =", CompressionErrors)
print("H1 compression error =", H1compressionErrors)

-----------------------------------
             Reduced basis accuracy
-----------------------------------
Offline Errors
L2 compression error = [4.464044518872249e-06, 3.2948510494119077e-06, 2.290250760263449e-06, 1.4675605306478524e-06, 8.915840966017943e-07, 7.557337160345698e-07, 1.0344107189534399e-06, 1.4292838150362181e-06, 1.839008459568471e-06, 2.260403555595182e-06, 5.6419137062022195e-06, 3.0292521469093515e-06, 2.638316514323157e-06, 2.9735064128926414e-06, 3.1133058130523887e-06, 3.071241432378604e-06, 3.0903399820237977e-06, 3.4597258065352133e-06, 4.385570343265425e-06, 5.914766889195202e-06, 6.466039662035052e-06, 2.2828281714074417e-06, 3.719265675449631e-06, 4.07665068527531e-06, 3.733074907693582e-06, 3.3228223211801526e-06, 3.0466976159325082e-06, 2.8139328856957787e-06, 2.7628176702476534e-06, 4.417740843515894e-06, 6.878018194786359e-06, 4.343622954486847e-06, 5.6479455139503074e-06, 4.983955485533267e-06, 4.643064369149396e-06, 4.356187524270617e-06, 4.068903388344752e-06, 4.325844963914229e-06, 4.463838149770944e-06, 5.069891550394366e-06, 7.252968845644662e-06, 7.1161760893413015e-06, 5.825223666363488e-06, 4.9078655698982395e-06, 6.477105587901556e-06, 5.629623181915566e-06, 3.435985246077469e-06, 3.666591301006391e-06, 4.3126374326462805e-06, 4.930810679681774e-06, 7.765028936646113e-06, 1.0758022999484722e-05, 5.765204954875644e-06, 6.881897092663532e-06, 9.823215026672102e-06, 7.545388982775257e-06, 4.999511761920188e-06, 6.2501806939502245e-06, 5.956618838091266e-06, 5.393592227066799e-06, 8.420860575531317e-06, 1.530055486222261e-05, 6.4263504016545675e-06, 8.70585220712872e-06, 1.093564578021148e-05, 5.531485466266688e-06, 4.88248204190274e-06, 7.536660217951777e-06, 5.605723600322189e-06, 6.0522794976476525e-06, 1.0317064718671453e-05, 2.021801920620293e-05, 7.932386218640109e-06, 1.0181978031918473e-05, 1.1198354134825336e-05, 5.04031183625094e-06, 8.884774594090558e-06, 8.337725622089321e-06, 3.784191171541323e-06, 7.740234225023708e-06, 1.7396051662626773e-05, 2.490089983737211e-05, 1.0177261735907454e-05, 1.2420205045018331e-05, 1.2400700091772858e-05, 9.107295330219943e-06, 1.322879798530576e-05, 8.462896334417241e-06, 8.136017146956131e-06, 7.40573172752598e-06, 3.319480686662823e-05, 2.8865070992548302e-05, 1.2764357020348877e-05, 1.617969257609354e-05, 1.3977194466545846e-05, 1.1508196930328714e-05, 1.4325893468926336e-05, 9.600712833369026e-06, 1.5911312993681602e-05, 1.1185720850111092e-05]
H1 compression error = [2.7252932301889785e-05, 2.013750683906652e-05, 1.402739794153043e-05, 9.03203033440891e-06, 5.587308491638867e-06, 4.914693737670023e-06, 6.770478212303681e-06, 9.377848140674137e-06, 1.2148886270479421e-05, 1.5068140469183008e-05, 3.538305427019371e-05, 1.9688743754521856e-05, 1.6012708785290326e-05, 1.733984297032835e-05, 1.82353361047264e-05, 1.8227299129936916e-05, 1.8412505197389516e-05, 2.0446903618376864e-05, 2.592173938409032e-05, 3.554656059492433e-05, 3.796273452215796e-05, 1.4436918887135222e-05, 2.092649864639411e-05, 2.3683363675915002e-05, 2.2848548422432317e-05, 2.1207231093055237e-05, 1.9563234268424123e-05, 1.7360600445174253e-05, 1.6053761582722742e-05, 2.7579187022371675e-05, 3.8786741840616874e-05, 2.2619722066470402e-05, 3.0113021819023792e-05, 2.82996368838738e-05, 2.656205066435831e-05, 2.4652490908009468e-05, 2.3350583231630944e-05, 2.454448822024161e-05, 2.4121676619653384e-05, 3.0152660068145523e-05, 4.10866622034923e-05, 3.35404485790202e-05, 3.063462381567499e-05, 2.689789093972627e-05, 3.151552824445099e-05, 2.6637387395390685e-05, 1.79925538977804e-05, 2.1420579255796295e-05, 2.294970681598286e-05, 2.71096597917859e-05, 4.436459028061381e-05, 4.8435418288719186e-05, 3.0530069439965646e-05, 3.176041176607239e-05, 4.273556464930769e-05, 3.2664926942419514e-05, 2.3358513419411886e-05, 3.154113689298677e-05, 2.9969324371075966e-05, 2.759118381889585e-05, 4.675240980636919e-05, 6.692728464979316e-05, 3.213983174315306e-05, 3.521510054042893e-05, 4.534984712394246e-05, 2.3582656573518707e-05, 2.1276223191861312e-05, 3.4513081768172264e-05, 2.7845065891343934e-05, 2.9574001635377192e-05, 5.137634592785078e-05, 8.61508367731696e-05, 3.6750504542604e-05, 4.031827349791773e-05, 4.714785992850797e-05, 2.1638631599125338e-05, 3.3666719164020035e-05, 3.3455551337854464e-05, 1.983379959723556e-05, 3.3242891489082104e-05, 7.50107840515417e-05, 0.0001031623106532193, 4.529839464211873e-05, 5.104232741789849e-05, 5.126545848318776e-05, 3.3064402356663036e-05, 4.785757709236215e-05, 3.1064457959244826e-05, 3.314365823126774e-05, 2.8298730472408282e-05, 0.00013533770789244366, 0.0001159802382139368, 5.6097565167957744e-05, 6.732256071877919e-05, 5.852998165365517e-05, 5.1157193654252605e-05, 5.843907884382997e-05, 3.895991570740153e-05, 6.118099709629903e-05, 4.0914896868423286e-05]

Construction of the sensors

We now characterize our measures:

$$\forall m=1,\dots,M, y_m^{obs}=l^0_m(u^{true}(\mu^{true})).$$

Here $y_m^{obs}(\mu^{true})$ is the value of the $m$-th observation, with $l_m^0 \in \mathcal{U}'$. The form of the observation functional depends on the specific transducer used to acquire data. For instance, if the transducer measures a local state value, then we may model the observation as a Gaussian convolution (like in this example).

$$\forall m=1,\dots,M, \ q_m = R_u l^0_m,$$

which is the Riesz representation of the functional. The condition $(q_m,v)=l_m^0(v)$ is equivalent to the resolution of the following problem:

Find $q_m \in \mathcal{U}$ such that: $M q_m=b$ with $M $ the mass matrix and $b$ the vector obtained from the observation functionals $b_i=l_m^0(v_i)$ where $v_i$ are the FEM test functions.

$$\forall M=1,\dots,M_{max},\ \mathcal{U}_M=\textrm{Span} \{ q_m\}_{m=1}^M. $$

We want to choose the localization of the sensors adequately in order to get $M$ small such that $(q_m)_{m=1}^M \in (\mathcal{U}_M)^M$ forms a canonical reduced basis of the observation spaces.

$$l_m(v)=l_m(v,x_m,r_w)=C \int_{\Omega} exp \Big(-\frac{1}{2r_w^2}\lVert x-x_m \rVert_2^2 \Big) v(x) \ \textrm{dx},$$

where $C$ is such that $l_m(1)=1$, and $x_m$ corresponds to the center of the $m$-sensor localization. In this example, we take these centers as the nodes of a coarser mesh but we could have used a GEIM to choose them more wisely.

rw = 0.01  # deviation r_w
xm = np.array([0.5, 0.5]) #for test

## Normalization of the gaussian functionals
## lm = C exp(-||x - x_m||^2 / (2 * r_w^2))
def gaussian_weight_noC(x,xm):
    """ Gaussian function centered on xm of deviation rw (to find C for normalization). """
    return np.exp(-0.5 * np.sum((x - xm[:, np.newaxis,np.newaxis])**2, axis=0) / rw**2)

def compute_C(xm):
    # takes xm center of sensor as parameter
    
    @LinearForm
    def lm(v, w):
        """ Computes lm(1) by integrating the Gaussian weight. """
 
        weight = gaussian_weight_noC(w.x,xm)
        return weight * v 

    # Assemble the linear form for lm(1)
    b = lm.assemble(FineBasis)
    lm_value = np.sum(b)  # Numerical integration result: b@1 since v=1
    
    C = 1.0 / lm_value  # Scaling factor
    return C

C = compute_C(xm)

# lm = C exp(-||x - x_m||^2 / (2 * r_w^2))
def gaussian_weight(x,xm,C):
    """ Gaussian function centered on xm of deviation rw """
    return C*np.exp(-0.5 * np.sum((x - xm[:, np.newaxis,np.newaxis])**2, axis=0) / rw**2)

def qm_b(xm,C):
    # weak formulation
    # mass matrix : (v, q_m)_U
    
    @LinearForm
    def rhs(v, w):
        """ Right hand side  """
        weight = gaussian_weight(w.x,xm,C)  # Applique la gaussienne
        return weight * v

    # Assembling
    b = rhs.assemble(FineBasis)

    # Solving the linear system
    from scipy.sparse.linalg import spsolve
    #from scipy.sparse.linalg import cg
    #q_m, info = cg(M, b)
    #if info == 0:
    #    print("Conjugate Gradient converged!")
    #else:
    #    print("Conjugate Gradient did not converge.")
    
    q_m = spsolve(massMat, b)  # Resolution of M @ q_m = b
    return q_m

## sensors localizations from a coarser mesh M=25
## Coarse MESH
CoarseMesh = skfem.MeshTri().refined(3).with_boundaries(                                                                
        {                                                                                                                                
            "left": lambda x: x[0] == 0,                                                                                            
            "right": lambda x: x[0] == 1,            
            "down": lambda x: x[1] == 0,                                                                                            
            "up": lambda x: x[1] == 1,     
            
        }                                                                                                                               
)

xm_grid=CoarseMesh.p #xm = nodes

qmReducedBasis=np.zeros((num_dofs_uFineMesh,CoarseMesh.p.shape[1]))
for i in range(CoarseMesh.p.shape[1]):
    xm=xm_grid[:,i] #update xm
    C = compute_C(xm) # find C for normalization
    qmReducedBasis[:,i]=qm_b(xm,C) # generate qm
        
plot(FineMesh, qmReducedBasis[:,0], shading='gouraud', colorbar=True)
 
    
NumberOfModesM=np.shape(qmReducedBasis)[1]

Now we have two kind of basis functions:

the $\Phi_i \in \mathcal{Z}_N$ for $i=1,\dots,N$ (in the code bkReducedBasis),
the $q_j \in \mathcal{U}_M$, for $j=1,\dots,M$ (in the code qmReducedBasis).

PBDW algebraic form

We now state the algebraic form of the PBDW problem. We need to compute $(A)_{i,j}=(q_i,q_j)$ and $(B)_{i,j}=(\Phi_j,q_i)$ and to assemble $K= \begin{pmatrix} A & B \\ B^T & 0 \end{pmatrix}$ in order to solve $\begin{pmatrix} \eta_M \\ z_N \end{pmatrix} = K^{-1} \begin{pmatrix} y^{obs} \\ 0 \end{pmatrix}$

print("---------------------------------------")
print("---- OFFLINE PART: BUILDING MATRICES --")
print("---------------------------------------")

############################################
## Assembling A and B  #####################
############################################

AMat=massMat
BMat=massMat

#############################################
## Project matrices onto the reduced space ##
#############################################

Areduced = np.zeros((NumberOfModesM, NumberOfModesM))
Breduced = np.zeros((NumberOfModesM, NumberOfModesN))

for i in range(NumberOfModesM):
    MatAVecProduct = AMat.dot(qmReducedBasis[:,i])
    for j in range(NumberOfModesM):
        if j>=i:
            Areduced[i,j] = np.dot(qmReducedBasis[:,j],MatAVecProduct)
            Areduced[j,i] = Areduced[i,j]

for i in range(NumberOfModesM):
    MatBVecProduct = BMat.dot(qmReducedBasis[:,i])
    for j in range(NumberOfModesN):
        Breduced[i,j] = np.dot(bkReducedBasis[j],MatBVecProduct)
         
            

Kreduced = np.zeros((NumberOfModesN+NumberOfModesM,NumberOfModesN+ NumberOfModesM))
#fill global K
for i in range(NumberOfModesM):
    for j in range(NumberOfModesM):
        Kreduced[i,j]=Areduced[i,j]

for i in range(NumberOfModesM):
    for j in range(NumberOfModesN):
        Kreduced[i,NumberOfModesM+j]=Breduced[i,j]
        Kreduced[NumberOfModesM+j,i]=Breduced[i,j]

---------------------------------------
---- OFFLINE PART: BUILDING MATRICES --
---------------------------------------

ONLINE PART

We can now solve $\begin{pmatrix} \eta_M \\ z_N \end{pmatrix} = K^{-1} \begin{pmatrix} y^{obs} \\ 0 \end{pmatrix}$ and we approximate $u^{true}(\mu^{true})$ by $u=\sum_{i=1}^M (\eta_M)_i \ q_i +\sum_{i=1}^N (z_N)_i \ \Phi_i $.

# Construct the observations from the basis (qm)_m and the true data
yobs = np.zeros(NumberOfModesM)

#mu1=mu1list[1]
#mu2=mu2list[2]
#utrue=SolveAdvDiffProblem([mu1,mu2],FineMesh,True)
# Compute (q_m, v)_U
qm_inner =utrue@ massMat

for i in range(NumberOfModesM):    
    yobs[i] =qm_inner.dot(qmReducedBasis[:,i])


print("--------------------------------------------")
print("---- ONLINE PART: SOLVING REDUCED PROBLEM --")
print("--------------------------------------------")

RightHandSide= np.concatenate([yobs, np.zeros(NumberOfModesN)]) 
## SOLVING PROBLEM ##
#EtaZ=np.linalg.solve(Kreduced,RightHandSide)
#Eta, Z = EtaZ[:NumberOfModesM],EtaZ[NumberOfModesM:]
#print("norm",np.linalg.norm(Areduced@Eta+Breduced@Z-yobs))
#print("norm",np.linalg.norm(Breduced.transpose()@Eta))
#u=np.dot(Eta,qmReducedBasis.transpose())+np.dot(Z,bkReducedBasis)

Z=np.linalg.solve(np.dot(np.dot(Breduced.transpose(),np.linalg.inv(Areduced)),Breduced),np.dot(np.dot(Breduced.transpose(),np.linalg.inv(Areduced)),yobs))
Eta=np.linalg.solve(Areduced,yobs-np.dot(Breduced,Z))
u=np.dot(Eta,qmReducedBasis.transpose())+np.dot(Z,bkReducedBasis)

ExactSolution =utrue #exact observations
ReconstructedCompressedSolution = u #reduced solution
    
norml2ExactSolution=np.sqrt(ExactSolution@(L2massMat@ExactSolution))
normh1ExactSolution=np.sqrt(ExactSolution@(massMat@ExactSolution))
t=np.abs(ReconstructedCompressedSolution-ExactSolution)

if norml2ExactSolution !=0 and normh1ExactSolution != 0:
    relError=np.sqrt(t@L2massMat@t)/norml2ExactSolution
    relh1Error=np.sqrt(t@massMat@t)/normh1ExactSolution
    
else:
    relError = np.linalg.norm(ReconstructedCompressedSolution-ExactSolution)
    
print("L2 relative error: ",relError)
print("H1 relative error: ",relh1Error)

plot(FineMesh,utrue, shading='gouraud', colorbar=True)
plot(FineMesh,u, shading='gouraud', colorbar=True)
#plot(FineMesh,np.dot(Z,bkReducedBasis)-utrue, shading='gouraud', colorbar=True)
plot(FineMesh,u-utrue, shading='gouraud', colorbar=True)

--------------------------------------------
---- ONLINE PART: SOLVING REDUCED PROBLEM --
--------------------------------------------
L2 relative error:  0.0004129386415760589
H1 relative error:  0.003935527762515732





<Axes: >