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Photon transport through a diffusing slab can be described by the radiative transfer equation (RTE). When the slab is highly scattering and weakly absorbing, the RTE simplifies to the diffusion equation. In this paper, an inverse diffusion…

A method is presented for the determination of complex-valued compression and shear elastic moduli of polymers for ultrasound applications. The resulting values, which are scarcely reported in the literature, are found with uncertainties…

Fluid Dynamics · Physics 2025-10-30 William N. Bodé , Fabian Lickert , Per Augustsson , Henrik Bruus

There are now many options for doubly robust estimation; however, there is a concerning trend in the applied literature to believe that the combination of a propensity score and an adjusted outcome model automatically results in a doubly…

A pre-trained unconditional diffusion model, combined with posterior sampling or maximum a posteriori (MAP) estimation techniques, can solve arbitrary inverse problems without task-specific training or fine-tuning. However, existing…

Machine Learning · Computer Science 2026-02-09 Sai Bharath Chandra Gutha , Ricardo Vinuesa , Hossein Azizpour

The typical transverse momentum of a quark in the proton is a basic property of any QCD based model of nucleon structure. However, calculations in phenomenological models typically give rather small values of transverse momenta, which are…

High Energy Physics - Phenomenology · Physics 2022-02-01 A. I. Signal , F. G. Cao

A solid object's geometry, density, and elastic moduli completely determine its spectrum of normal modes. Solving the inverse problem - determining a material's elastic moduli given a set of resonance frequencies and sample geometry -…

Strongly Correlated Electrons · Physics 2023-01-23 Avi Shragai , Florian Theuss , Gael Grissonnanche , B. J. Ramshaw

Reconstructing medical images from partial measurements is an important inverse problem in Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). Existing solutions based on machine learning typically train a model to directly map…

Image and Video Processing · Electrical Eng. & Systems 2022-06-17 Yang Song , Liyue Shen , Lei Xing , Stefano Ermon

The transverse-momentum-dependent distributions (TMDs), which are defined by gauge-invariant 3D parton correlators with staple-shaped lightlike Wilson lines, can be calculated from quark and gluon correlators fixed in the Coulomb gauge on a…

High Energy Physics - Phenomenology · Physics 2024-12-04 Yong Zhao

This revisit gives a survey on the analytical methods for the inverse exponential Radon transform which has been investigated in the past three decades from both mathematical interests and medical applications such as nuclear medicine…

Image and Video Processing · Electrical Eng. & Systems 2020-02-06 Jason You

We propose and investigate efficient numerical methods for inverse problems related to Magnetic Resonance Imaging (MRI). Our goal is to extend the recent convergence results for the Landweber-Kaczmarz method obtained in [Haltmeier, Leitao,…

Numerical Analysis · Mathematics 2020-12-22 A. Leitao , J. Zubelli

In this paper, we present a dimension reduction method to reduce the dimension of parameter space and state space and efficiently solve inverse problems. To this end, proper orthogonal decomposition (POD) and radial basis function (RBF) are…

Numerical Analysis · Mathematics 2016-10-18 Fuchen Chen , Lijian Jiang , Guanghui Zheng

We consider the inverse problem of reconstructing inhomogeneities by performing a finite number of scattering measurements of acoustic type in the time-harmonic setting. We set up the reconstruction as a fully discrete variational problem…

Analysis of PDEs · Mathematics 2026-02-24 Daniela Di Donato , Luca Rondi

In this paper, a reconstruction method for the spatially distributed dielectric constant of a medium from the back scattering wave field in the frequency domain is considered. Our approach is to propose a globally convergent algorithm,…

Analysis of PDEs · Mathematics 2016-03-01 Michael V. Klibanov , Loc H. Nguyen , Anders Sullivan , Lam Nguyen

Indentation test is used with growing popularity for the characterization of various materials on different scales. Developed methods are combining the test with computer simulation and inverse analyses to assess material parameters…

Computational Physics · Physics 2015-07-14 Vladimir Buljak , Shwetank Pandey

There have been increasing reports that the diffusion coefficient of macromolecules depends on time and fluctuates randomly. Here, a novel method to elucidate the fluctuating diffusivity from trajectory data is developed. The time-averaged…

Statistical Mechanics · Physics 2017-10-25 Tomoshige Miyaguchi

We develop Monte Carlo simulations for uniformly charged polymers and machine learning algorithm to interpret the intra-polymer structure factor of the charged polymer system, which can be obtained from small-angle scattering experiments.…

Soft Condensed Matter · Physics 2025-11-21 Lijie Ding , Chi-Huan Tung , Jan-Michael Y. Carrillo , Wei-Ren Chen , Changwoo Do

Polymer mixtures fractionate between phases depending on their molecular weight. Consequently, by varying solvent conditions, a polydisperse polymer sample can be separated between phases so as to achieve a particular molecular weight…

Soft Condensed Matter · Physics 2026-04-10 J. Pedro de Souza , William M. Jacobs , Howard A. Stone

Regression evaluation has been performed for decades. Some metrics have been identified to be robust against shifting and scaling of the data but considering the different distributions of data is much more difficult to address (imbalance…

Machine Learning · Computer Science 2020-09-14 Mario Michael Krell , Bilal Wehbe

Differential spectra in observables that resolve additional soft or collinear QCD emissions exhibit Sudakov double logarithms in the form of logarithmic plus distributions. Important examples are the total transverse momentum $q_T$ in…

High Energy Physics - Phenomenology · Physics 2017-02-24 Markus A. Ebert , Frank J. Tackmann

Mixture proportion estimation (MPE) is the problem of estimating the weight of a component distribution in a mixture, given samples from the mixture and component. This problem constitutes a key part in many "weakly supervised learning"…

Machine Learning · Computer Science 2016-06-01 Harish G. Ramaswamy , Clayton Scott , Ambuj Tewari