2.2 Modeling the solution using multiscale basis
Selecting the dominant scale corresponding to the small eigen values gives rise to a fixed basis sets and using fixed basis to solve the weak form produces the fixed solution un,fixed(x,t) = ﰄ n n,ωj n H n,ωj i,j βi,j φi (x,t), where βi,j’s are defined in each computational time interval and φi (x,t) are fixed basis functions. Fixed solution at n + 1 th time point is computed by solving equation
(3) by setting un as the fixed solution at n th time point and writing un+1 in the space of HH
5
1 0.9
0.8ωE K 0.7
0.6
0.5
0.4
0.3
KK 1ω2 i 0.2 KK
0.1
0
0 0.2 0.4 0.6 0.8 1
Figure 3: Illustration of fine grid, coarse grid, coarse neighborhood and oversampled domain. fixed basis at n + 1 th time
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The true solution is assumed to be normal around the fixed solution with small variance. Finally, this structure enables us to compute the posterior or conditional distribution of the basis selection probability and conditional solution of the system given the observation and the pde model.
Residual and selection probability on the subregion and basis
From equation (3), the residual is defined as
Rn(un+1,un+1 ,un
) =
ﰆ
Ω fn+1v − ﰆ
ﰆ un+1+un+1−un
+ fixed fixed v v + fixed fixed
Ω n+1
∆t
+ ufixed) · ∇v.
(4)
+ κ∇(u+ n+1
Ω
6
For any fixed basis φn,ωj ’s this equation is zero as the fixed solution is constructed by setting the k n,ωj equation zero for each fixed basis. Using φk,+ ’s ∀k, j in the residual function one can compute the residual for additional basis and writing down the residual as a long vector over subregions and basis the following quantity is defined. Let αωk = ∥Rnωk ∥/∥Rn∥, where Rn is the global residual vector and Rnωk is the local residual vector in ωk (as mentioned earlier) and L1 norm is used .
Let Nω be the average number of subregions where additional basis will added. Furthermore, αωj αﰇ j = ﰄj αωj Nω, (5) ω With probability proportional to αﰇ j ∧ 1, the region ωj , is selected and Jj = 1 if the region is ω selected and zero otherwise. Given subregion j is selected the k th extra basis is
The problem I am going to work on is #68 on page 539 . The
to calculate error derivative of current time. This is a modification of back propagation algorithm and known as back
with the quantization step of ${\rm N} = \left[ {\frac{H}{{\Delta Y_k}}} \right]$. The nodes are divided into ${L}$ steps and we decide whether node $j$ is selected
$$\frac{\partial \mathbf{u}}{\partial x} = \frac{\partial \mathbf{u}}{\partial r} \frac{\partial r}{\partial x} + \frac{\partial \mathbf{u}}{\partial s} \frac{\partial s}{\partial x} + \frac{\partial \mathbf{u}}{\partial t}
Let us suppose that Hamiltonian of the system has a form $\hat{H}\big(p,q,\lambda \big)$. Here $p,q$ are canonical coordinates and $\lambda$ is the parameter. For the solution of Schrodinger equation $\imath \frac{\partial \Psi}{\partial t}=\hat{H}\Psi$ we implement following ansatz: $\Psi =\sum_{n}a_{n}\big(t\big)\varphi_{n}\big(p,q,\lambda\big)\exp \big\{-\imath \int_{-\infty}^{t}E_{n}\big(\lambda\big)dt\big\}$, where $E_{n}\big(\lambda\big)$ are the instantaneous quasi-energies that adiabatically depend on the parameter $\lambda$. After standard derivations for time dependent coefficients $a_{n}\big(t\big)$ we obtain iterative solution $a_{n}^{(1)}\big(t\big)=-\int_{-\infty}^{t}d\tau\sum_{m\neq n}\frac{\big\langle\varphi_{n}\big|\frac{\partial H}{\partial \lambda}\big|\varphi_{m}\big\rangle\dot{\lambda}}{E_{m}-E_{n}}\times a_{m}\big(-\infty\big)\exp \big\{-\imath \int_{-\infty}^{\tau}\big(E_{m}-E_{n}\big)d\acute{\tau}\big\}$. Adiabatic approximation is valid when the following criteria holds $\frac{a_{n}^{(2)}}{a_{n}^{(1)}}\sim \frac{\partial H}{\partial t}\frac{1}{\big(E_{m}-E_{n}\big)^{2}}$. Here $a_{n}^{(2)}$ is the second order correction to $a_{n}\big(t\big)$.
The trace statistics ʎ trace and the maximum Eigen statistics ʎ max were used and the results are presented in table 3 and 4 below.
Fick’s second law states how the concentration of a chemical changes with time because of diffusion. Let c(x, y) denote the concentration at position (x, y) ∈ Ω. The steady state version of Fick’s second law (without interior sources of the chemical) is Laplace’s equation ∂2c ∂2c + 2 = 0. ∂x2 ∂y Consider a problem with domain Ω = {(x, y) : 0 ≤ x ≤ 1, xi = ih, i = 0, . . . , n 0 ≤ y ≤ 1.5} . j = 0, . . . , (3.1)
u and v are found by solving the equation shown in (3.6). Ix and Iy are the hori-
Based on the above analysis we suggest that the solution provided by Coopers and Myers are
In the direction of numerical study of singularly perturbed partial differential equations with time delay, much can be seen in \cite{57,235,236}, and the references therein. In particular, the authors in \cite{235} designed parameter uniform numerical methods using the fitted mesh and fitted operator approach, respectively, which results in uniform convergence of first order in time and second order in spatial direction. High order numerical methods are of great interest for the numerical community. They are fairly understood for singularly perturbed problems without delay, see \cite{221,237,238,239} and the references therein. Nevertheless, attempt of having higher order of uniform convergence
If, moreover, the mapping is strictly monotone, then zero is the unique solution. These results
There exist number of papers where similar problem is studied. In \cite{170}, first order uniform convergence was proved for the central finite difference scheme constructed on a Shishkin mesh; this result was improved in \cite{171} showing that the central difference scheme really is an almost second order uniformly convergent scheme. In \cite{172}, for reaction diffusion systems with an arbitrary number of equations, second order of uniform convergence of central differences scheme was proved. In \cite{173} the analysis was extended to a parabolic reaction diffusion system of equations, proving first order uniform convergence for the scheme combining the Euler method in time and central differences in space. In the context of FEM, up to our knowledge only the $p$ and $hp-$version approximates the solution at an exponential rate of convergence. In \cite{174} some numerical results showing the efficiency of this method were giving for the case of two equations. On the other hand, in \cite{175} an arbitrary number of equations is considered, proving the convergence in the simpler case of equal diffusion parameters.