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Inverse Source Problems for Wave Propagation
Peijun Li
Department of Mathematics
Purdue University
Joint Work with
G. Bao, C. Chen, G. Yuan, Y. Zhao
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Outline
Motivation and model problems
Inverse random source
Increasing stability
Ongoing and future work
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Source scattering problems
Source scattering problems are concerned with the relationship betweenradiating sources and wave fields.
Direct problem: To determine the wave field from the given sourceand the differential equation governing the wave motion.
Inverse problem: To determine the radiating source which producesthe measured wave field.
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Wave equations for source scattering
The Helmholtz equation - acoustic wave
∆u + κ2u = f in Rd .
The Navier equation - elastic wave
µ∆u + (λ+ µ)∇∇ · u + ω2u = f in Rd .
The Maxwell equations - electromagnetic wave
∇× E − iωµH = 0, ∇×H + iωεE = J in R3.
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Problem geometry
ΓR
source
Ω
BR
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Applications
Antenna synthesis
Tomography (PAT)
Medical imaging (MEG, EEG, ENG)
Fluorescence microscopy
Neuroscience (brain imaging)
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Related work
Deterministic problems
Devaney and Sherman (’82), Marengo and Devaney (’99), Ammari,Bao, and Fleming (’02), Fokas, Kurylev, and Marinakis (’04), Hauer,Kuhn, Potthast (’05), Albanese and Monk (’06), Devaney, Marengo,and Li (’07), Eller and Valdivia (’09), Bao, Lin, and Triki (’10), Badiaand Nara (’11), Tittelfitz (’15), Bao, Lu, Rundell, and Xu (’15),Zhang and Guo (’15), Bao, L., Lin, and Triki (’15), Cheng, Isakov,and Lu (’16)
Stochastic problems
Devaney (’79), L. (’11), Bao and Xu (’13), Bao, Chow, L., and Zhou(’14), Bao, Chen, and L. (’16)
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Inverse random source problem
Homogeneous media
Inhomogeneous media
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Why stochastic modeling?
Uncertainties are widely introduced to the mathematical models for threemajor reasons:
Randomness directly appears in the studied systems
Incomplete knowledge of the systems may be modeled by uncertainties
Stochastic techniques are introduced to couple the interferencebetween different scales
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Available framework
Homogenization theory: J. Keller, J. Lions, G. Papanicolaou
Bayesian statistics: D. Donoho, A. Stuart, E. Somersalo
Wiener chaos expansion: R. Cameron, W. Martin, G. Karniadakis
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Model problem
The Helmholtz equation
∆u(x , κ) + κ2u(x , κ) = f (x), x ∈ R2.
The source functionf (x) = g(x) + σ(x)Wx .
The Sommerfeld radiation condition
limr→∞
r1/2(∂ru − iκu) = 0, r = |x |.
The direct problem: Given g and σ, to determine the random wave field u.
The inverse problem: To recover g and σ2 from u|ΓRat κj , j = 1, . . . ,m.
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White noise
Wx is the 2-parameter Brownian motion on (R2, B(R2), µ).
White noise
Wx :=∂2Wx
∂x1∂x2.
Stochastic integral ∫R2
φ(x)dWx =
∫R2
Wx∂2φ(x)
∂x1∂x2dx .
Proposition
E
∫R2
φ(x)dWx = 0, E∣∣∣∫
R2
φ(x)dWx
∣∣∣2 =
∫R2
|φ(x)|2dx .
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Deterministic direct problem
Consider the deterministic scattering problem∆u + κ2u = g , x ∈ R2,
∂ru − iκu = o(r−1/2), r →∞.
Given g ∈ L2(Ω), it has a unique solution
u(x) =
∫ΩG (x , y)g(y)dy ,
where the Green function
G (x , y) = − i
4H
(1)0 (κ|x − y |).
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Stochastic direct problem
Consider the stochastic scattering problem∆u + κ2u = g + σWx , x ∈ R2,
∂ru − iκu = o(r−1/2), r →∞.
Theorem (Bao-Chen-L)
There exists a unique continuous stochastic process (mild solution) u,which satisfies
u(x) =
∫ΩG (x , y)g(y)dy +
∫ΩG (x , y)σ(y)dWy .
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Function regularity
g ∈ L2(Ω).
σ is chosen such that the stochastic integral∫ΩG (x , y , κ)σ(y)dWy
satisfies
E∣∣∫
ΩG (x , y , κ)σ(y)dWy
∣∣2 =
∫Ω|G (x , y , κ)|2σ2(y)dy <∞.
Hence σ ∈ Lp(Ω), p > 2 and σ ∈ C 0,η(Ω), 0 < η < 1.
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Constructive proof
Step 1: continuous modification of the Gaussian random field
v(x) =
∫ΩG (x , y)σ(y)dWy .
Step 2: construct an approximation sequence
W nx =
n∑j=1
|Kj |−12 ξjχKj
(x), ξj = |Kj |−12
∫Kj
dWx , ξj ∼ N (0, 1).
Step 3: consider an approximated solution
un(x) =
∫ΩG (x , y)g(y)dy +
∫ΩG (x , y)σ(y)dW n
x .
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Stochastic inverse problem
Recall the mild solution
u(x , κj) =
∫ΩG (x , y , κj)g(y)dy +
∫ΩG (x , y , κj)σ(y)dWy .
Taking the expectation yields
Eu(x , κj) =
∫ΩG (x , y , κj)g(y)dy .
Consider real and imaginary parts
ReG (x , y , κj) =1
4Y0(κj |x − y |), ImG (x , y , κj) = −1
4J0(κj |x − y |).
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Reconstruction of the mean
Real-valued Fredholm integral equations
EReu(x , κj) =1
4
∫ΩY0(κj |x − y |)g(y)dy ,
EImu(x , κj) = −1
4
∫ΩJ0(κj |x − y |)g(y)dy .
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Reconstruction of the variance
Taking the variance of the mild solution yields
VReu(x , κj) =1
16
∫ΩY 2
0 (κj |x − y |)σ2(y)dy ,
VImu(x , κj) =1
16
∫ΩJ2
0 (κj |x − y |)σ2(y)dy .
VReu(x , κj)−VImu(x , κj) =1
16
∫Ω
(Y 2
0 (κj |x − y |)− J20 (κj |x − y |)
)σ2(y)dy
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Numerical method - Kaczmarz algorithm
Consider the linear system of equations
Ajq = pj , j = 1, . . . ,m.
A1q = p1
A2q = p2A3q = p3
q0
q1q2
q3
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Regularized Kaczmarz algorithm
Let q0 = 0, do k = 0, 1, . . .q0 = qk ,
qj = qj−1 + ATj (µI + AjA
Tj )−1(pj − Ajqj−1), j = 1, . . . ,m,
qk+1 = qm,
where µ > 0 is a regularization parameter.
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Direct solver
Consider the approximated scattering problem∆u + κ2u = g + σW n
x , x ∈ R2,
∂ru − iκu = o(r−1/2), r →∞,
where
W nx =
n∑j=1
|Kj |−12 ξjχKj
(x).
Direct solver: FEM with PML, Monte Carlo.
Parameters: κj = (j − 0.5)π, j = 1, . . . , 5, µ = 1.0× 10−7, outloop k = 5.
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Numerical result
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Inhomogeneous media: direct problem
Consider the stochastic scattering problem∆u + κ2(1 + q)u = g + σWx , x ∈ R2,
∂ru − iκu = o(r−1/2), r →∞.
Theorem (Bao-Chen-L)
The stochastic scattering problem admits a unique continuous stochasticprocess u, which satisfies
u(x) = −κ2
∫ΩG (x , y)q(y)u(y)dy
+
∫ΩG (x , y)g(y)dy +
∫ΩG (x , y)σ(y)dWy .
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Inhomogeneous media: inverse problem
Consider the inhomogeneous stochastic Helmholtz equation
∆u(x , κ) + κ2(1 + q(x))u(x , κ) = g(x) + σ(x)Wx in BR .
Let v be the eigenfunction for the following problem:∆v(x , κ) + κ2(1 + q(x))v(x , κ) = 0 inBR ,
v(x , κ) = 0 on ΓR .
We have from the integation by parts that
−∫
ΓR
∂νv(x , κ)u(x , κ)dγ =
∫BR
g(x)v(x , κ)dx +
∫BR
σ(x)v(x , κ)dWx .
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Numerical result - homogeneous medium
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Numerical result - inhomogeneous medium
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Increasing stability
Continuous frequency data
Discrete frequency data
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Problem geometry
ΓR
source
Ω
BR
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Model problem
The Helmholtz equation
∆u(x , κ) + κ2u(x , κ) = f (x), x ∈ R2.
The Sommerfeld radiation condition
limr→∞
r1/2(∂ru − iκu) = 0, r = |x |.
Given f ∈ L2(Ω), it has a unique solution:
u(x , κ) =
∫ΩG (x , y ;κ)f (y)dy ,
where
G (x , y ;κ) = − i
4H
(1)0 (κ|x − y |).
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Transparent boundary condition
Given function u on ΓR , it has the Fourier series expansion:
u(R, θ) =∑n∈Z
un(R)e inθ, un(R) =1
2π
∫ 2π
0u(R, θ)e−inθdθ.
Introduce the DtN operator T : H12 (ΓR)→ H−
12 (ΓR)
(Tu)(R, θ) = κ∑n∈Z
H(1)′n (κR)
H(1)n (κR)
un(R)e inθ.
Transparent boundary condition:
∂νu = Tu on ΓR .
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Problem formulation - continuous frequency data
Consider reduced problem∆u + κ2u = f in BR ,
∂νu = Tu on ΓR .
Define boundary data
‖u(·, κ)‖2ΓR
=
∫ΓR
(|Tu(x , κ)|2 + κ2|u(x , κ)|2
)dγ(x).
ISP 1. Let f be a complex function with a compact support Ω ⊂ BR . TheISP is to determine f from the data u(x , κ), x ∈ ΓR , κ ∈ (0,K ), whereK > 1 is a positive constant.
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Main result - ISP 1
Define
FM = f ∈ Hm(Ω) : ‖f ‖Hm(BR) ≤ M, suppf = Ω ⊂ BR,
where m > 2 is an integer and M > 1 is a constant.
Theorem (L-Yuan)
Let f ∈ FM and u be the solution of the scattering problem correspondingto f . Then
‖f ‖2L2(Ω) . ε2 +
M2(K
23 | ln ε|
14
(6m−15)3
)2m−5,
where
ε =
(∫ K
0κ‖u(·, κ)‖2
ΓRdκ
) 12
.
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Sketch of proof
Step 1: energy estimate
‖f ‖2L2(Ω) .
∫ ∞0
κ‖u(·, κ)‖2ΓRdκ.
Step 2: low frequency estimate
I1(s) =
∫ s
0κ3
∫ΓR
∣∣∣∣∫ΩH
(1)0 (κ|x − y |)f (y)dy
∣∣∣∣2 dγ(x)dκ,
I2(s) =
∫ s
0κ
∫ΓR
∣∣∣∣∫Ω∂νxH
(1)0 (κ|x − y |)f (y)dy
∣∣∣∣2 dγ(x)dκ.
Step 3: high frequency tail estimate∫ +∞
sκ‖u(·, κ)‖2
ΓRdκ . s−(2m−5)‖f ‖2
Hm(Ω).
Step 4: link between low and high frequencies.
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Problem formulation - discrete frequency data
Consider reduced problem∆u + κ2u = f in BR ,
∂νu = Tu on ΓR .
Define boundary data at discrete frequency
‖u(·, κn)‖2ΓR
=
∫ΓR
(|Tu(x , κn)|2 + κ2
n|u(x , κn)|2)dγ(x),
whereκn = n
(πR
), n = |n|, n ∈ Z2 \ 0.
ISP 2. Let f be a complex function with a compact support Ω ⊂ BR . TheISP is to determine f from the datau(x , κ), x ∈ ΓR , κ ∈ (0, πR ] ∪ ∪Nn=1κn, where 1 < N ∈ N.
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Main result - ISP 2
Define
FM = f ∈ FM :
∫Ωf (x)dx = 0.
Theorem (L-Yuan)
Let f ∈ FM and u be the solution of the scattering problem correspondingto f . Then
‖f ‖2L2(Ω) . ε2
1 +M2(
N58 | ln ε2|
19
(6m−15)3
)2m−5,
where
ε1 =
∑n≤N‖u(·, κn)‖2
ΓR
12
, ε2 = supκ∈(0,π
R]‖u(·, κ)‖ΓR
.
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Sketch of proof
Step 1: energy estimate
|fn|2 . ‖u(·, κn)‖2ΓR, n ∈ Z2 \ 0.
Step 2: low frequency estimate
I (s) =
∣∣∣∣∫BR
f (x)e−isx ·ddx
∣∣∣∣2 , s ∈ (0,π
R].
Step 3: high frequency tail estimate
∞∑n=N0
|fn|2 ≤ N−(2m−5)0 ‖f ‖2
Hm(BR).
Step 4: link between low and high frequencies.
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References
(with G. Bao and C. Chen) Inverse random source scatteringproblems in several dimensions, SIAM/ASA J. UncertaintyQuantitication, 2016.
(with G. Bao and C. Chen) Inverse random source scattering forelastic waves, preprint.
(with G. Bao and C. Chen) Inverse random source scattering for theHelmholtz equation in inhomogeneous media, preprint.
(with G. Yuan) Stability on the inverse random source scatteringproblem for the one-dimensional Helmholtz equation, J. Math. Anal.Appl., to appear.
(with G. Yuan) Increasing stability for the inverse source scatteringproblem with multi-frequencies, preprint.
(with G. Bao and Y. Zhao) Stability in the inverse source problem forelastic and electromagnetic waves with multi-frequencies, preprint.
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Ongoing and future work
Partial data
Inhomogeneous media
Inverse random medium problem
Time-domain inverse problems
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Thank You !