A Bound-Plus-Equality Constrained Quadratic Minimization Algorithm for Inverse Problems
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optimization, inverse problems↗
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Stochastic inverse problems: Models and metrics
In past work, we introduced model-based inverse methods, and applied them to problems in which the anomaly could be reasonably modeled by simple canonical shapes, such as rectangular solids. In these cases the parameters to be inverted would be length, width and height, as well as the occasional probe lift-off or rotation. We are now developing a formulation that allows more flexibility in modeling complex flaws. The idea consists of expanding the flaw in a sequence of basis functions, and then solving for the expansion coefficients of this sequence, which are modeled as independent random variables, uniformly distributed over their range of values. There are a number of applications of such modeling: 1. Connected cracks and multiple half-moons, which we have noted in a POD set. Ideally we would like to distinguish connected cracks from one long shallow crack. 2. Cracks of irregular profile and shape which have appeared in cold work holes during bolt-hole eddy-current inspection. One side of such cracks is much deeper than other. 3. L or C shaped crack profiles at the surface, examples of which have been seen in bolt-hole cracks. By formulating problems in a stochastic sense, we are able to leverage the stochastic global optimization algorithms in NLSE, which is resident in VIC-3D®, to answer questions of global minimization and to compute confidence bounds using the sensitivity coefficient that we get from NLSE. We will also address the issue of surrogate functions which are used during the inversion process, and how they contribute to the quality of the estimation of the bounds.
36 MATERIALS SCIENCE↗
Physics-informed neural networks for inverse problems in nano-optics and metamaterials
In this paper, we employ the emerging paradigm of physics-informed neural networks (PINNs) for the solution of representative inverse scattering problems in photonic metamaterials and nano-optics technologies. In particular, we successfully apply mesh-free PINNs to the difficult task of retrieving the effective permittivity parameters of a number of finite-size scattering systems that involve many interacting nanostructures as well as multi-component nanoparticles. Our methodology is fully validated by numerical simulations based on the finite element method (FEM). The development of physics-informed deep learning techniques for inverse scattering can enable the design of novel functional nanostructures and significantly broaden the design space of metamaterials by naturally accounting for radiation and finite-size effects beyond the limitations of traditional effective medium theories.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Deep Equilibrium Learning of Explicit Regularization Functionals for Imaging Inverse Problems
Not Available
97 MATHEMATICS AND COMPUTING↗
Direct and inverse problems in radiation of sound from discrete random sources on two coaxial rings
An analytical model consisting of two ring sources of sound is developed to study the direct radiation in terms of correlation, coherence, and phase and also to aid in solving the inverse-radiation problem of determining the noise source in terms of farfield measurements. The rings consist of discrete sources which are either monopoles or quadrupoles with Gaussian autocorrelations. Only adjacent sources, both within and between the rings, are correlated. Results show that from the farfield information one can determine when the sources are compact or noncompact with respect to the acoustic wavelength and distinguish between the types of sources. In addition, from the inverse-radiation approach one can recover the center of mass, the location and separation distance of the ring, and the respective diameters.
Maestrello, L.↗
Some aspects of the inversion problem in remote sensing
Several commonly used methods for inversion--constrained linear inversion, synthesis (Backus-Gilbert) methods and nonlinear iterative techniques for the Chahine type--are discussed. It is demonstrated that a very close connection exists between Backus-Gilbert solutions and those given by constrained linear inversion. A number of examples of the application of such methods are presented, showing that resolution is not greatly different for quite different algorithms, a result quite in accord with general theoretical considerations: more resolution can be achieved at the expense of introducing greater a priori bias in the procedure.
Twomey, S.↗
Randomized physics-informed machine learning for uncertainty quantification in high-dimensional inverse problems
Not Available
97 MATHEMATICS AND COMPUTING↗
Final Technical Report for "Applied Mathematics Research: Simulation Based Optimization and Application to Electromagnetic Inverse Problems"
The focus of research was: Developing adaptive mesh for the solution of Maxwell's equations; Developing a parallel framework for time dependent inverse Maxwell's equations; Developing multilevel methods for optimization problems with inequality constraints; A new inversion code for inverse Maxwell's equations in the 0th frequency (DC resistivity); A new inversion code for inverse Maxwell's equations in low frequency regime. Although the research concentrated on electromagnetic forward and in- verse problems the results of the research was applied to the problem of image registration.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Improved Bayesian regularization of inverse problems in vibrations and acoustics using noise-only measurements
Here, this paper studies Tikhonov regularization (ridge regression) parameter selection for problems in vibrations and acoustics. The selection method is based on a popular Bayesian method, but it incorporates measurements of sensor noise. The regularization parameter is closely related to the ratio of system input energy to noise energy, so noise measurements inform the inference procedure and improve parameter identification. In cases where standard Bayesian regularization identifies zero as the optimal regularization parameter, noise measurements guarantee a unique nonzero optimum. Sufficient theoretical criteria are developed for this guarantee. The method is verified in even-determined and under-determined configurations in an acoustic source localization simulation and a vibration load identification experiment. It is shown to yield significant improvements over existing empirical Bayesian regularization. Improvements are larger in the even-determined case and smaller in the under-determined case, wherein the inverse solution is less sensitive to the regularization parameter.
42 ENGINEERING↗
Sequential ensemble transform for Bayesian inverse problems
In this work, we present the Sequential Ensemble Transform (SET) method, an approach for generating approximate samples from a Bayesian posterior distribution. The method explores the posterior distribution by solving a sequence of discrete optimal transport problems to produce a series of transport plans which map prior samples to posterior samples. We prove that the sequence of Dirac mixture distributions produced by the SET method converges weakly to the true posterior as the sample size approaches infinity. Furthermore, our numerical results indicate that, when compared to standard Sequential Monte Carlo (SMC) methods, the SET approach is more robust to the choice of Markov mutation kernels and requires less computational efforts to reach a similar accuracy when used to explore complex posterior distributions. Finally, we describe adaptive schemes that allow to completely automate the use of the SET method.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Inverse problems in radiative transfer - Determination of atmospheric parameters
Relaxation method for inverse radiative transfer equation solution, determining temperature profiles and atmospheric parameters
Chahine, M. T.↗
The P prime(f) to N(h) inversion problem in ionospheric soundings
A general review is given of the inversion techniques used to derive the ionospheric electron density N as a function of altitude h from group path P versus frequency f measurements obtained by vertical incidence ionospheric sounders. The paper discusses the medium under investigation, the experimental techniques used to obtain the P(f) data, the theoretical considerations leading to the integral equation relating P(f) to N(h), and the assumptions made in the inversion process. The lamination inversion technique is then presented, with special attention given to mathematical difficulties arising from discontinuities in the P(f) function, infinities in the integrand, and in some cases unknown integration limits. Methods outlined for minimizing the uncertainties due to discontinuities include the use of redundant information - that is the two distinct P(f) functions available for a given N(h) profile - and the use of models based on statistical data. Mathematical procedures are discussed that increase significantly the efficiency and accuracy of the required numerical integrations. The accuracy of the inversion technique is deduced by comparing the resulting N(h) profile with N(h) data obtained by simultaneous but independent observations.
Jackson, J. E.↗
Alpert multi-wavelets for functional inverse problems: direct optimization and deep learning
Computational engineering models often contain unknown entities (e.g. parameters, initial and boundary conditions) that require estimation from other measured observable data. Estimating such unknown entities is challenging when they involve spatio-temporal fields because such functional variables often require an infinite-dimensional representation. Here, we address this problem by transforming an unknown functional field using Alpert wavelet bases and truncating the resulting spectrum. Hence the problem reduces to the estimation of few coefficients that can be performed using common optimization methods. We apply this method on a one-dimensional heat transfer problem where we estimate the heat source field varying in both time and space. The observable data is comprised of temperature measured at several thermocouples in the domain. This latter is composed of either copper or stainless steel. The optimization using our method based on wavelets is able to estimate the heat source with an error between 5% and 7%. We analyze the effect of the domain material and number of thermocouples as well as the sensitivity to the initial guess of the heat source. Finally, we estimate the unknown heat source using a different approach based on deep learning techniques where we consider the input and output of a multi-layer perceptron in wavelet form. We find that this deep learning approach is more accurate than the optimization approach with errors below 4%.
97 MATHEMATICS AND COMPUTING↗
Inverse problems and optimal experiment design in unsteady heat transfer processes identification
Experimental-computational methods for estimating characteristics of unsteady heat transfer processes are analyzed. The methods are based on the principles of distributed parameter system identification. The theoretical basis of such methods is the numerical solution of nonlinear ill-posed inverse heat transfer problems and optimal experiment design problems. Numerical techniques for solving problems are briefly reviewed. The results of the practical application of identification methods are demonstrated when estimating effective thermophysical characteristics of composite materials and thermal contact resistance in two-layer systems.
Artyukhin, Eugene A.↗
Modelling of radiative transfer by the Monte Carlo method and solving the inverse problem based on a genetic algorithm according to experimental results of aerosol sensing on short paths using a femtosecond laser source
We consider the algorithms that implement a broadband ('multiwave') radiative transfer with allowance for multiple (aerosol) scattering and absorption by main atmospheric gases. In the spectral range of 0.6 – 1 μm, a closed numerical simulation of modifications of the supercontinuum component of a probing femtosecond pulse is performed. In the framework of the algorithms for solving the inverse atmospheric-optics problems with the help of a genetic algorithm, we give an interpretation of the experimental backscattered spectrum of the supercontinuum. An adequate reconstruction of the distribution mode for the particles of artificial aerosol with the narrow-modal distributions in a size range of 0.5 – 2 mm and a step of 0.5 mm is obtained. (light scattering)
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
The inverse problem: Ocean tides derived from earth tide observations
Indirect mapping ocean tides by means of land and island-based tidal gravity measurements is presented. The inverse scheme of linear programming is used for indirect mapping of ocean tides. Open ocean tides were measured by the numerical integration of Laplace's tidal equations.
Kuo, J. T.↗
A statistical approach for isolating fossil fuel emissions in atmospheric inverse problems
We study independent verification and quantification of fossil fuel (FF) emissions that constitutes a considerable scientific challenge. By coupling atmospheric observations of CO 2 with models of atmospheric transport, inverse models offer the possibility of overcoming this challenge. However, disaggregating the biospheric and FF flux components of terrestrial fluxes from CO 2 concentration measurements has proven to be difficult, due to observational and modeling limitations. In this study, we propose a statistical inverse modeling scheme for disaggregating winter time fluxes on the basis of their unique error covariances and covariates, where these covariances and covariates are representative of the underlying processes affecting FF and biospheric fluxes. The application of the method is demonstrated with one synthetic and two real data prototypical inversions by using in situ CO 2 measurements over North America. Also, inversions are performed only for the month of January, as predominance of biospheric CO 2 signal relative to FF CO 2 signal and observational limitations preclude disaggregation of the fluxes in other months. The quality of disaggregation is assessed primarily through examination of a posteriori covariance between disaggregated FF and biospheric fluxes at regional scales. Findings indicate that the proposed method is able to robustly disaggregate fluxes regionally at monthly temporal resolution with a posteriori cross covariance lower than 0.15 µmol m -2 s -1 between FF and biospheric fluxes. Error covariance models and covariates based on temporally varying FF inventory data provide a more robust disaggregation over static proxies (e.g., nightlight intensity and population density). However, the synthetic data case study shows that disaggregation is possible even in absence of detailed temporally varying FF inventory data.
54 ENVIRONMENTAL SCIENCES↗
The inverse problem of constructing a gravimetric geoid
Computation of a single geoidal height from gravity acceleration data formally requires that the latter be known everywhere on the earth. A computational procedure based on linear inverse theory for estimating geoidal heights from incomplete sets of data is presented. The same scheme can be used to estimate gravity accelerations from altimetry-derived geoids. The systematic error owing to lack of data and the choice of a particular inverse operator is described by using resolution functions and their spherical harmonic expansions. An rms value of this error is also estimated by assuming a spectrum for the unknown geoid. The influence of the size of the data region, the spacing between data, the filtering applied to the data, and the model weighting function chosen are all quantified in a spherical geometry. The examples presented show that when low degree spherical harmonic coefficients are available - from satellite orbit analysis - a band-passed version of the geoid can be constructed from local gravity data, even with a relatively restricted data set.
Zlotnicki, V.↗