674 research outputs found
Ensemble Kalman filter for neural network based one-shot inversion
We study the use of novel techniques arising in machine learning for inverse
problems. Our approach replaces the complex forward model by a neural network,
which is trained simultaneously in a one-shot sense when estimating the unknown
parameters from data, i.e. the neural network is trained only for the unknown
parameter. By establishing a link to the Bayesian approach to inverse problems,
an algorithmic framework is developed which ensures the feasibility of the
parameter estimate w.r. to the forward model. We propose an efficient,
derivative-free optimization method based on variants of the ensemble Kalman
inversion. Numerical experiments show that the ensemble Kalman filter for
neural network based one-shot inversion is a promising direction combining
optimization and machine learning techniques for inverse problems
Numerical identification of a nonlinear diffusion law via regularization in Hilbert scales
We consider the reconstruction of a diffusion coefficient in a quasilinear
elliptic problem from a single measurement of overspecified Neumann and
Dirichlet data. The uniqueness for this parameter identification problem has
been established by Cannon and we therefore focus on the stable solution in the
presence of data noise. For this, we utilize a reformulation of the inverse
problem as a linear ill-posed operator equation with perturbed data and
operators. We are able to explicitly characterize the mapping properties of the
corresponding operators which allow us to apply regularization in Hilbert
scales. We can then prove convergence and convergence rates of the regularized
reconstructions under very mild assumptions on the exact parameter. These are,
in fact, already needed for the analysis of the forward problem and no
additional source conditions are required. Numerical tests are presented to
illustrate the theoretical statements.Comment: 17 pages, 2 figure
Edge-promoting reconstruction of absorption and diffusivity in optical tomography
In optical tomography a physical body is illuminated with near-infrared light
and the resulting outward photon flux is measured at the object boundary. The
goal is to reconstruct internal optical properties of the body, such as
absorption and diffusivity. In this work, it is assumed that the imaged object
is composed of an approximately homogeneous background with clearly
distinguishable embedded inhomogeneities. An algorithm for finding the maximum
a posteriori estimate for the absorption and diffusion coefficients is
introduced assuming an edge-preferring prior and an additive Gaussian
measurement noise model. The method is based on iteratively combining a lagged
diffusivity step and a linearization of the measurement model of diffuse
optical tomography with priorconditioned LSQR. The performance of the
reconstruction technique is tested via three-dimensional numerical experiments
with simulated measurement data.Comment: 18 pages, 6 figure
A new approach to nonlinear constrained Tikhonov regularization
We present a novel approach to nonlinear constrained Tikhonov regularization
from the viewpoint of optimization theory. A second-order sufficient optimality
condition is suggested as a nonlinearity condition to handle the nonlinearity
of the forward operator. The approach is exploited to derive convergence rates
results for a priori as well as a posteriori choice rules, e.g., discrepancy
principle and balancing principle, for selecting the regularization parameter.
The idea is further illustrated on a general class of parameter identification
problems, for which (new) source and nonlinearity conditions are derived and
the structural property of the nonlinearity term is revealed. A number of
examples including identifying distributed parameters in elliptic differential
equations are presented.Comment: 21 pages, to appear in Inverse Problem
Expectation Propagation for Nonlinear Inverse Problems -- with an Application to Electrical Impedance Tomography
In this paper, we study a fast approximate inference method based on
expectation propagation for exploring the posterior probability distribution
arising from the Bayesian formulation of nonlinear inverse problems. It is
capable of efficiently delivering reliable estimates of the posterior mean and
covariance, thereby providing an inverse solution together with quantified
uncertainties. Some theoretical properties of the iterative algorithm are
discussed, and the efficient implementation for an important class of problems
of projection type is described. The method is illustrated with one typical
nonlinear inverse problem, electrical impedance tomography with complete
electrode model, under sparsity constraints. Numerical results for real
experimental data are presented, and compared with that by Markov chain Monte
Carlo. The results indicate that the method is accurate and computationally
very efficient.Comment: Journal of Computational Physics, to appea
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