Stochastic PDEs and their numerical approximation

106 Pages·2013·2.61 MB·English

Stochastic PDEs and their numerical approximation
Gabriel Lord
Maxwell Institute, Heriot Watt University, Edinburgh
[email protected], http://www.ma.hw.ac.uk/ gabriel
∼
◮ Based on
Introduction to Computational Stochastic Partial Differential
Equations
G. J. Lord, C. E. Powell, T. Shardlow
CUP.
To appear : 2014.
Informal description of SPDEs and numerical approximation.
a b
7 7

1
6 6
5 5
0.5
4 4
0
y 3 3
2 2 −0.5
1 1
−1
0 0
0 2 4 6 0 2 4 6
x x
(a) Deterministic vorticity (b) Stochastic
Informal description of SPDEs and numerical approximation.
a b
7 7 1
6 6
5 5 0.5
4 4
0
y 3 y 3
2 2
−0.5
1 1
0 0 −1
0 2 4 6 0 2 4 6
x x
(a) Stochastic (Rough) (b) Stochastic (Smooth)
Informal : will cut some corners !
Some (other) reference books for SPDEs
◮ Semigroup approach to SPDEs
◮ Classic reference :
Da Prato, Giuseppe and Zabczyk, Jerzy
Stochastic Equations in Infinite Dimensions
Encyclopedia of Mathematics and its Applications
CUP, 1992. ISBN : 0-521-38529-6
◮ Chow, Pao-Liu
Stochastic Partial Differential Equations
Chapman & Hall/CRC, Boca Raton, FL
2007, ISBN 978-1-58488-443-9; 1-58488-443-6
◮ Variational approach
◮ Pr´evˆot, Claudia and Ro¨ckner, Michael
A concise course on stochastic partial differential equations
Springer,2007, ISBN = 978-3-540-70780-6; 3-540-70780-8.
◮ Numerical methods
◮ Jentzen, Arnulf and Kloeden, Peter E.
Taylor approximations for stochastic partial differential equations
CBMS-NSF Regional Conference Series in Applied Mathematics
SIAM, 2011, ISBN : 978-1-611972-00-9
◮ Physics approaches
◮ Garc´ıa-Ojalvo, Jordi and Sancho, Jos´e M.
Noise in spatially extended systems
Springer, ISBN 0-387-98855-6
◮ C. Gardiner
Stochastic Methods: A handbook for the natural and social
sciences
Springer Series in Synergetics
2009, ISBN 978-3-540-70712-7
◮ SDEs : plenty of choice.
◮ Øksendal, Bernt,Stochastic Differential Equations,2003.
3-540-04758-1
Background
◮ PDEs
◮ ODEs
◮ SDEs
PDE
Many physical/biological models are described by parabolic PDEs
u = [∆u+f(u)] u(0) = u0given u D (1)
t
∈
+ BCs on D bounded specified. f(u) given where u(t,x)
Two typical examples:
◮ Nagumo equation
u = [u +u(1 u)(u α)] u(x,t) R, x [0,L], t > 0
t xx
− − ∈ ∈
◮ Allen-Cahn equation
u = u +u u3 u(x,t) R, x [0,2π), t > 0
t xx
− ∈ ∈
◮ We writehsemilinear PDiEs of form
u = ∆u+f(u)
t
as ODE on Hilbert space H (eg L2(D)).
du
= Au+f(u)
dt −
A = ∆
−
u = Au+f(u)
t
−
Note - we could write solution in three ways :
◮Integrate :
t
u(t) = u(0)+ ( Au+f(u))ds
−
Z0
Too restrictive on regularity of u(t).
◮Weak solution (multiply by test fn. Integ. by parts).
du(s)
,v = a(u(s),v)+ f(u(s)),v , v V,
dt − ∀ ∈
(cid:28) (cid:29)
(cid:10) (cid:11)
where a(u,v) := A1/2u,A1/2v
◮Variation of constants
(cid:10) (cid:11)
t
u(t) = e tAu(0)+ e (t s)Af(u(s))ds
− − −
Z0
need to understand semigroup e tA and its properties.
−
PDE as infinite system of ODEs
u = Au+f(u) u(0) = u0
t
−
◮ Look at weak solution
◮ Write u as a infinte series
u(x,t) = u φ (x)
k k
k Z
X∈
with φ e.func. and λ e.val of A (on D +BCs)
k k
◮ Subst. into PDE, take inner-product with φ
k
du
k = λ u +f (u), k Z, f(u) = f (u)φ .
k k k k k
dt − ∈
k
X
Get infinite system of ODEs.
(truncation leads to spectral Galerkin approximation).
◮ Let’s look at adding noise to ODE
ODEs SDEs & Brownian Motion
→
In each Fourier mode have ODE of the form :Let’s add noise
du dβ
= λu+f(u)+g(u)
dt dt
with β (t) Brownian motion.
k
β = (β (t),β (t), ,β (t)), t 0
1 2 n
··· ≥
Is a (standard) Brownian motion or a Wiener process if for each β
j
◮ have β(0) = 0
◮ Increments β(t) β(s) are normal N(0,t s), for 0 s t.
− − ≤ ≤
Equivalently β(t) β(s) √t sN(0,1).
− ∼ −
◮ Increments β(t) β(s) and β(τ) β(σ) are independent
− −
0 s t σ τ.
≤ ≤ ≤ ≤
Note : β(t) = β(t) 0 = β(t) β(0) N(0,t).
− − ∼
So E β(t) = 0 and variance var(β(t)) = E β(t)2 = t.
(cid:2) (cid:3) (cid:2) (cid:3)