Inverse Problems最新文献_第4页

Inverse conductivity problem with one measurement: uniqueness of multi-layer structures 一次测量的反电导率问题：多层结构的唯一性

IF 2.1 2区数学

Inverse Problems Pub Date : 2024-07-02 DOI: 10.1088/1361-6420/ad5b82

Lingzheng Kong, Youjun Deng and Liyan Zhu

引用次数: 0

Bayesian interface technique-based inverse estimation of closure coefficients of standard k−ϵ turbulence model by limiting the number of DNS data points for flow over a periodic hill 基于贝叶斯界面技术的标准 k-ϵ 湍流模型闭合系数的反估计，方法是限制周期性山丘上流动的 DNS 数据点数量

IF 2.1 2区数学

Inverse Problems Pub Date : 2024-07-01 DOI: 10.1088/1361-6420/ad5a34

Nagendra Kumar Chaurasia and Shubhankar Chakraborty

{"title":"Bayesian interface technique-based inverse estimation of closure coefficients of standard k−ϵ turbulence model by limiting the number of DNS data points for flow over a periodic hill","authors":"Nagendra Kumar Chaurasia and Shubhankar Chakraborty","doi":"10.1088/1361-6420/ad5a34","DOIUrl":"https://doi.org/10.1088/1361-6420/ad5a34","url":null,"abstract":"Among different numerical methods for modeling turbulent flow, Reynolds-averaged Navier–Stokes (RANS) is the most commonly used and computationally reasonable. However, the accuracy of RANS is lower than that of other high-fidelity numerical methods. In this work, the uncertainties associated with the coefficients of the standard RANS turbulence model are estimated and calibrated to improve the accuracy. The calibration is performed by considering the coefficients individually as well as collectively. The first three coefficients of the standard turbulence model are calibrated among the five coefficients ( and σk ). The Bayesian inference technique using the Metropolis–Hastings algorithm is applied to quantify uncertainties and calibration. Flow over a periodic hill is selected as a test case. The separation height of the bubble at and , along with the streamwise velocity at various locations, has been chosen as the quantities of interest for comparing the results with DNS. The calibration is performed using known high-fidelity data (direct numerical simulation) from the available data set. The velocity field is re-calculated from the calibrated closure coefficients and compared with the same calculated with the standard coefficients of turbulence model (baseline). The deviation of calibrated Cµ is almost 50%–60% from baseline and for and it is 3%–12% and 6%–9% respectively. The algorithm is tested for different Reynold numbers and data points. A sensitivity analysis is also performed.","PeriodicalId":50275,"journal":{"name":"Inverse Problems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141511937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Solving, tracking and stopping streaming linear inverse problems 解决、跟踪和停止流线性逆问题

IF 2.1 2区数学

Inverse Problems Pub Date : 2024-06-25 DOI: 10.1088/1361-6420/ad5583

Nathaniel Pritchard and Vivak Patel

引用次数: 0

Piecewise nonlinear materials and Monotonicity Principle 片断非线性材料和单调性原理

IF 2.1 2区数学

Inverse Problems Pub Date : 2024-06-23 DOI: 10.1088/1361-6420/ad575c

Antonio Corbo Esposito, Luisa Faella, Vincenzo Mottola, Gianpaolo Piscitelli, Ravi Prakash and Antonello Tamburrino

引用次数: 0

Direct inversion of the Longitudinal ray transform for 2D residual elastic strain fields 直接反演二维残余弹性应变场的纵向射线变换

IF 2.1 2区数学

Inverse Problems Pub Date : 2024-06-17 DOI: 10.1088/1361-6420/ad52bb

C M Wensrich, S Holman, M Courdurier, W R B Lionheart, A P Polyakova and I E Svetov

引用次数: 0

A novel Newton method for inverse elastic scattering problems 用于反弹性散射问题的新型牛顿法

IF 2.1 2区数学

Inverse Problems Pub Date : 2024-06-11 DOI: 10.1088/1361-6420/ad4dda

Yan Chang, Yukun Guo, Hongyu Liu and Deyue Zhang

引用次数: 0

Reconstruction of inhomogeneous media by an iteration algorithm with a learned projector 利用迭代算法和学习投影器重建非均质介质

IF 2.1 2区数学

Inverse Problems Pub Date : 2024-06-10 DOI: 10.1088/1361-6420/ad4f0b

Kai Li, Bo Zhang, Haiwen Zhang

{"title":"Reconstruction of inhomogeneous media by an iteration algorithm with a learned projector","authors":"Kai Li, Bo Zhang, Haiwen Zhang","doi":"10.1088/1361-6420/ad4f0b","DOIUrl":"https://doi.org/10.1088/1361-6420/ad4f0b","url":null,"abstract":"This paper is concerned with the inverse problem of reconstructing an inhomogeneous medium from the acoustic far-field data at a fixed frequency in two dimensions. This inverse problem is severely ill-posed (and also strongly nonlinear), and certain regularization strategy is thus needed. However, it is difficult to select an appropriate regularization strategy which should enforce some <italic toggle=\"yes\">a priori</italic> information of the unknown scatterer. To address this issue, we plan to use a deep learning approach to learn some <italic toggle=\"yes\">a priori</italic> information of the unknown scatterer from certain ground truth data, which is then combined with a traditional iteration method to solve the inverse problem. Specifically, we propose a deep learning-based iterative reconstruction algorithm for the inverse problem, based on a repeated application of a deep neural network and the iteratively regularized Gauss–Newton method (IRGNM). Our deep neural network (called the learned projector in this paper) mainly focuses on learning the <italic toggle=\"yes\">a priori</italic> information of the <italic toggle=\"yes\">shape</italic> of the unknown contrast with a normalization technique in the training processes and is trained to act like a projector which is helpful for projecting the solution into some feasible region. Extensive numerical experiments show that our reconstruction algorithm provides good reconstruction results even for the high contrast case and has a satisfactory generalization ability.","PeriodicalId":50275,"journal":{"name":"Inverse Problems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141511943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Feasibility of acousto-electric tomography 声电断层扫描的可行性

IF 2.1 2区数学

Inverse Problems Pub Date : 2024-06-03 DOI: 10.1088/1361-6420/ad4669

Bjørn Jensen, Adrian Kirkeby and Kim Knudsen

{"title":"Feasibility of acousto-electric tomography","authors":"Bjørn Jensen, Adrian Kirkeby and Kim Knudsen","doi":"10.1088/1361-6420/ad4669","DOIUrl":"https://doi.org/10.1088/1361-6420/ad4669","url":null,"abstract":"In acousto-electric tomography (AET) the goal is to reconstruct the electric conductivity in a domain from electrostatic boundary measurements of corresponding currents and voltages, while the domain is perturbed by a time-dependent acoustic wave, thus taking advantage of the acousto-electric effect. We approach the AET reconstruction in two steps: First, the interior power density is obtained from boundary measurements by solving a linear inverse and ill-posed problem; second, the interior conductivity is reconstructed from the power density by solving a non-linear and well-posed problem. Mathematically these inverse problems are fairly well understood, and reconstruction methods work well on synthetic data. This is in contrast to experimental findings. An effect can indeed be observed and data can be collected. However, the acousto-electric coupling is very weak, and consequently, the change in the measured voltage due to the acoustic perturbation might be too small compared to the background noise for viable reconstructions. In this paper, we take one step towards understanding the feasibility of AET. We provide an in-silico model of the coupled physics scenario based on standard models for the individual phenomena. Moreover, we formulate and implement numerically a full reconstruction method for the inverse problem via the two steps. We perform computational experiments with realistically chosen parameters from the context of medical imaging. The focus is on understanding the role of the acousto-electric coupling parameter and the signal-to-noise ratio (SNR). The critical signal strength is analyzed and the omnipresent Johnson–Nyquist noise is estimated. We obtain both positive and negative findings; we can reconstruct features even under severe noise conditions, but we also find that the SNR one is likely to face in practice is too low to obtain useful reconstructions.","PeriodicalId":50275,"journal":{"name":"Inverse Problems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141252489","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Uncertainty quantification for goal-oriented inverse problems via variational encoder-decoder networks 通过变分编码器-解码器网络实现面向目标的逆问题的不确定性量化

IF 2.1 2区数学

Inverse Problems Pub Date : 2024-06-03 DOI: 10.1088/1361-6420/ad5373

B. Afkham, Julianne Chung, Matthias Chung

{"title":"Uncertainty quantification for goal-oriented inverse problems via variational encoder-decoder networks","authors":"B. Afkham, Julianne Chung, Matthias Chung","doi":"10.1088/1361-6420/ad5373","DOIUrl":"https://doi.org/10.1088/1361-6420/ad5373","url":null,"abstract":"\u0000 In this work, we describe a new approach that uses variational encoder-decoder (VED) networks for efficient uncertainty quantification for goal-oriented inverse problems. Contrary to standard inverse problems, these approaches are goal-oriented in that the goal is to estimate some quantities of interest (QoI) that are functions of the solution of an inverse problem, rather than the solution itself. Moreover, we are interested in computing uncertainty metrics associated with the QoI, thus utilizing a Bayesian approach for inverse problems that incorporates the prediction operator and techniques for exploring the posterior. This may be particularly challenging, especially for nonlinear, possibly unknown, operators and nonstandard prior assumptions. We harness recent advances in machine learning, i.e., VED networks, to describe a data-driven approach to large-scale inverse problems. This enables a real-time uncertainty quantification for the QoI. One of the advantages of our approach is that we avoid the need to solve challenging inversion problems by training a network to approximate the mapping from observations to QoI. Another main benefit is that we enable uncertainty quantification for the QoI by leveraging probability distributions in the latent and target spaces. This allows us to efficiently generate QoI samples and circumvent complicated or even unknown forward models and prediction operators. Numerical results from medical tomography reconstruction and nonlinear hydraulic tomography demonstrate the potential and broad applicability of the approach.","PeriodicalId":50275,"journal":{"name":"Inverse Problems","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2024-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141271994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inversion of the attenuated momenta ray transform of planar symmetric tensors 平面对称张量的衰减矩射线变换反演

IF 2.1 2区数学

Inverse Problems Pub Date : 2024-05-27 DOI: 10.1088/1361-6420/ad49cc

Hiroshi Fujiwara, David Omogbhe, Kamran Sadiq and Alexandru Tamasan

引用次数: 0