MULTI-ELEMENT GENERALIZED POLYNOMIAL CHAOS FOR ...

910 XIAOLIANG WAN AND GEORGE EM KARNIADAKIS
which yields that
(2.38)
(πi+1,πi+1) ≤ ((τ − αi)πi, (τ − αi)πi).
The proof concludes by noting that a ≤ αi ≤ b.
Thus, if the element is small, the L2 norm of πi will lead to underflow quickly.
In practice, we rescale the random elements with finite boundaries by the following
linear transformation:
(2.39)
ξk,i = bk,i − ak,i
2
Yk,i + bk,i + ak,i
,
2
where we map the random variable ξ k defined in element k to a random variable Y k
defined in [−1, 1] d . The PDF of Y k can be obtained as
(2.40)
� �
�
¯f(y k) = det �
∂ξ �
k �
�∂y
�
k
ˆ f(ξk(y k)) = f(ξk(y k))
Pr(IBk =1)
d�
i=1
bk,i − ak,i
.
2
Compared to ξk, Y k are much more tractable for the numerical construction of orthogonal
polynomials as demonstrated in section 3.1.1. After such a transformation is
employed, we can apply the ME-gPC scheme with respect to the new random variable
Y k instead of the random variable ξk. Note that such a mapping is usually unnecessary
for random elements with at least one infinite boundary because these elements
can be related to a small probability Pr(IBk = 1).
We now summarize the overall ME-gPC algorithm.
Algorithm 2.1 (ME-gPC).
–Step 1: build a stochastic ODE system by gPC
–Step 2: perform the decomposition of random space adaptively
– time step i: from1to M
– loop all the random elements
– if η γ
k Pr(IBk =1)≥θ1 –
in element k, then
if rn ≥ θ2 · maxj=1,...,d rj, then
– split random dimension ξn into two equal ones ξn,1 and ξn,2
– map ξn,1 and ξn,2 to Yn,1 and Yn,2 defined on [−1, 1]
– construct one-dimensional orthogonal polynomials for Yn,1 and Yn,2
– end if
– construct d-dimensional orthogonal polynomials using tensor products
– map information to children elements
– end if
– update the information by the gPC method
– end element loop
– end time step
–Step 3: postprocessing stage
Remark 2.1 (C0 continuity in random space). In the ME-gPC scheme, we deal
with the stochastic problem locally without considering the interface between two
random elements; in other words, we split the original problem into N independent
ones. The reason is that all the statistics are in Lq sense, which means that the
statistics are determined by up to qth moments of the desired random field. Thus,
the continuity
(2.41)
uB1(ξ) =uB2(ξ), ξ ∈ ¯ B1 ∩ ¯ B2,