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Journal articles on the topic 'Implicit gradient reconstruction'

Author: Grafiati

Published: 6 September 2023

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1

Singh, Manish K., N. Munikrishna, V. Ramesh, and N. Balakrishnan. "Implicit gradient reconstruction (IGR) method for compressible flow simulation." Journal of Physics: Conference Series 822 (April 11, 2017): 012030. http://dx.doi.org/10.1088/1742-6596/822/1/012030.

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2

Dahlke, Taylor, Biondo Biondi, and Robert Clapp. "Applied 3D salt body reconstruction using shape optimization with level sets." GEOPHYSICS 85, no. 5 (2020): R437—R446. http://dx.doi.org/10.1190/geo2019-0352.1.

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As oil and gas extraction becomes more advanced, deep-water exploration becomes increasingly focused on imaging near or under complex salt geology, which necessitates detailed velocity models with strong contrast interfaces. These interfaces can be elegantly tracked using the level sets of an implicit surface. One can invert for the velocity model that best fits the recorded data in a full-waveform inversion (FWI) style objective function by reparameterizing the model in terms of an implicit surface representation of the salt interface. With this parameterization of the FWI objective function,

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3

Boscheri, Walter, Maurizio Tavelli, and Nicola Paoluzzi. "High order Finite Difference/Discontinuous Galerkin schemes for the incompressible Navier-Stokes equations with implicit viscosity." Communications in Applied and Industrial Mathematics 13, no. 1 (2022): 21–38. http://dx.doi.org/10.2478/caim-2022-0003.

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Abstract In this work we propose a novel numerical method for the solution of the incompressible Navier-Stokes equations on Cartesian meshes in 3D. The semi-discrete scheme is based on an explicit discretization of the nonlinear convective flux tensor and an implicit treatment of the pressure gradient and viscous terms. In this way, the momentum equation is formally substituted into the divergence-free constraint, thus obtaining an elliptic equation on the pressure which eventually maintains at the discrete level the involution on the divergence of the velocity field imposed by the governing e

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4

Chen, Chong, and Guoliang Xu. "Gradient-flow-based semi-implicit finite-element method and its convergence analysis for image reconstruction." Inverse Problems 28, no. 3 (2012): 035006. http://dx.doi.org/10.1088/0266-5611/28/3/035006.

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5

Zhu, Xiangyuan, Kehua Guo, Hui Fang, Rui Ding, Zheng Wu, and Gerald Schaefer. "Gradient-Based Graph Attention for Scene Text Image Super-resolution." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 3 (2023): 3861–69. http://dx.doi.org/10.1609/aaai.v37i3.25499.

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Scene text image super-resolution (STISR) in the wild has been shown to be beneficial to support improved vision-based text recognition from low-resolution imagery. An intuitive way to enhance STISR performance is to explore the well-structured and repetitive layout characteristics of text and exploit these as prior knowledge to guide model convergence. In this paper, we propose a novel gradient-based graph attention method to embed patch-wise text layout contexts into image feature representations for high-resolution text image reconstruction in an implicit and elegant manner. We introduce a

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Strauss, Thilo, and Taufiquar Khan. "Implicit Solutions of the Electrical Impedance Tomography Inverse Problem in the Continuous Domain with Deep Neural Networks." Entropy 25, no. 3 (2023): 493. http://dx.doi.org/10.3390/e25030493.

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Electrical impedance tomography (EIT) is a non-invasive imaging modality used for estimating the conductivity of an object Ω from boundary electrode measurements. In recent years, researchers achieved substantial progress in analytical and numerical methods for the EIT inverse problem. Despite the success, numerical instability is still a major hurdle due to many factors, including the discretization error of the problem. Furthermore, most algorithms with good performance are relatively time consuming and do not allow real-time applications. In our approach, the goal is to separate the unknown

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7

Zang, Miao, Huimin Xu, and Yongmei Zhang. "Kernel-Based Multiview Joint Sparse Coding for Image Annotation." Mathematical Problems in Engineering 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/6727105.

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It remains a challenging task for automatic image annotation problem due to the semantic gap between visual features and semantic concepts. To reduce the gap, this paper puts forward a kernel-based multiview joint sparse coding (KMVJSC) framework for image annotation. In KMVJSC, different visual features as well as label information are considered as distinct views and are mapped to an implicit kernel space, in which the original nonlinear separable data become linearly separable. Then, all the views are integrated into a multiview joint sparse coding framework aiming to find a set of optimal

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8

Bates, Oscar, Lluis Guasch, George Strong, et al. "A probabilistic approach to tomography and adjoint state methods, with an application to full waveform inversion in medical ultrasound." Inverse Problems 38, no. 4 (2022): 045008. http://dx.doi.org/10.1088/1361-6420/ac55ee.

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Abstract Bayesian methods are a popular research direction for inverse problems. There are a variety of techniques available to solve Bayes’ equation, each with their own strengths and limitations. Here, we discuss stochastic variational inference (SVI), which solves Bayes’ equation using gradient-based methods. This is important for applications which are time-limited (e.g. medical tomography) or where solving the forward problem is expensive (e.g. adjoint methods). To evaluate the use of SVI in both these contexts, we apply it to ultrasound tomography of the brain using full-waveform inversi

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9

Bi, Sheng, Jianzhong Zhou, Yi Liu, and Lixiang Song. "A Finite Volume Method for Modeling Shallow Flows with Wet-Dry Fronts on Adaptive Cartesian Grids." Mathematical Problems in Engineering 2014 (2014): 1–20. http://dx.doi.org/10.1155/2014/209562.

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A second-order accurate, Godunov-type upwind finite volume method on dynamic refinement grids is developed in this paper for solving shallow-water equations. The advantage of this grid system is that no data structure is needed to store the neighbor information, since neighbors are directly specified by simple algebraic relationships. The key ingredient of the scheme is the use of the prebalanced shallow-water equations together with a simple but effective method to track the wet/dry fronts. In addition, a second-order spatial accuracy in space and time is achieved using a two-step unsplit MUS

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10

PADOVANI, E., E. PRIOLO, and G. SERIANI. "LOW AND HIGH ORDER FINITE ELEMENT METHOD: EXPERIENCE IN SEISMIC MODELING." Journal of Computational Acoustics 02, no. 04 (1994): 371–422. http://dx.doi.org/10.1142/s0218396x94000233.

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The finite element method (FEM) is a numerical technique well suited to solving problems of elastic wave propagation in complex geometries and heterogeneous media. The main advantages are that very irregular grids can be used, free surface boundary conditions can be easily taken into account, a good reconstruction is possible of irregular surface topography, and complex geometries, such as curved, dipping and rough interfaces, intrusions, cusps, and holes can be defined. The main drawbacks of the classical approach are the need for a large amount of memory, low computational efficiency, and th

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11

Du, Tao, Kui Wu, Pingchuan Ma, et al. "DiffPD: Differentiable Projective Dynamics." ACM Transactions on Graphics 41, no. 2 (2022): 1–21. http://dx.doi.org/10.1145/3490168.

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We present a novel, fast differentiable simulator for soft-body learning and control applications. Existing differentiable soft-body simulators can be classified into two categories based on their time integration methods: Simulators using explicit timestepping schemes require tiny timesteps to avoid numerical instabilities in gradient computation, and simulators using implicit time integration typically compute gradients by employing the adjoint method and solving the expensive linearized dynamics. Inspired by Projective Dynamics ( PD ), we present Differentiable Projective Dynamics ( DiffPD

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12

Murthy, J. Y., and S. R. Mathur. "Computation of Anisotropic Conduction Using Unstructured Meshes." Journal of Heat Transfer 120, no. 3 (1998): 583–91. http://dx.doi.org/10.1115/1.2824315.

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This paper presents a conservative finite volume scheme for computing conduction heat transfer in materials with anisotropic conductivity. Unstructured solution-adaptive meshes composed of arbitrary convex polyhedra are used. Discrete energy balances are written over these polyhedra. Temperature gradients required for the evaluation of secondary diffusion fluxes are found by linear reconstruction. A fully implicit scheme is used for unsteady problems. The resulting discrete equations are solved using an algebraic multigrid scheme. Schemes for hanging-node and conformal adaption are implemented

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13

Zhong, Jiaping, Yunsong Li, Weiying Xie, Jie Lei, and Xiuping Jia. "Multi-Prior Twin Least-Square Network for Anomaly Detection of Hyperspectral Imagery." Remote Sensing 14, no. 12 (2022): 2859. http://dx.doi.org/10.3390/rs14122859.

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Anomaly detection of hyperspectral imagery (HSI) identifies the very few samples that do not conform to an intricate background without priors. Despite the extensive success of hyperspectral interpretation techniques based on generative adversarial networks (GANs), applying trained GAN models to hyperspectral anomaly detection remains promising but challenging. Previous generative models can accurately learn the complex background distribution of HSI and typically convert the high-dimensional data back to the latent space to extract features to detect anomalies. However, both background modeli

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14

Koos, Krisztian, Begüm Peksel, and Lóránd Kelemen. "Phase Measurement Using DIC Microscopy." Acta Cybernetica 23, no. 2 (2017): 629–43. http://dx.doi.org/10.14232/actacyb.23.2.2017.12.

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The development of fluorescent probes and proteins has helped make light microscopy more popular by allowing the visualization of specific subcellular components, location and dynamics of biomolecules. However, it is not always feasible to label the cells as it may be phototoxic or perturb their functionalities. Label-free microscopy techniques allow us to work with live cells without perturbation and to evaluate morphological differences, which in turn can provide useful information for high-throughput assays. In this study, we use one of the most popular label-free techniques called differen

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15

Studholme, Joshua, and Sergey Gulev. "Concurrent Changes to Hadley Circulation and the Meridional Distribution of Tropical Cyclones." Journal of Climate 31, no. 11 (2018): 4367–89. http://dx.doi.org/10.1175/jcli-d-17-0852.1.

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AbstractPoleward trends in seasonal-mean latitudes of tropical cyclones (TCs) have been identified in direct observations from 1980 to the present. Paleoclimate reconstructions also indicate poleward–equatorward migrations over centennial–millennial time scales. Hadley circulation (HC) is often both implicitly and explicitly invoked to provide dynamical linkages to these shifts, although no direct analysis of concurrent changes in the recent period has been presented. Here, the observational TC record (1981–2016) and ERA-Interim, JRA-55, and MERRA-2 are studied to examine potential relationshi

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16

Jin, Hu, and George A. McMechan. "Fast acoustic velocity tomography of focusing operators." GEOPHYSICS 79, no. 4 (2014): R121—R131. http://dx.doi.org/10.1190/geo2013-0389.1.

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A 2D velocity model was estimated by tomographic imaging of overlapping focusing operators that contain one-way traveltimes, from common-focus points to receivers in an aperture along the earth’s surface. The stability and efficiency of convergence and the quality of the resulting models were improved by a sequence of ideas. We used a hybrid parameterization that has an underlying grid, upon which is superimposed a flexible, pseudolayer model. We first solved for the low-wavenumber parts of the model (approximating it as constant-velocity pseudo layers), then we allowed intermediate wavenumber

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17

Karkalousos, Dimitrios, Samantha Noteboom, Hanneke E. Hulst, Frans M. Vos, and Matthan W. A. Caan. "Assessment of data consistency through cascades of independently recurrent inference machines for fast and robust accelerated MRI reconstruction." Physics in Medicine & Biology, May 4, 2022. http://dx.doi.org/10.1088/1361-6560/ac6cc2.

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Abstract Objective: Machine Learning methods can learn how to reconstruct Magnetic Resonance Images (MRI) and thereby accelerate acquisition, which is of paramount importance to the clinical workflow. Physics-informed networks incorporate the forward model of accelerated MRI reconstruction in the learning process. With increasing network complexity, robustness is not ensured when reconstructing data unseen during training. We aim to embed data consistency (DC) in deep networks while balancing the degree of network complexity. While doing so, we will assess whether either explicit or implicit e

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18

Torres, Kristian, and Mauricio Sacchi. "Least-squares reverse time migration via deep learning-based updating operators." GEOPHYSICS, August 4, 2022, 1–80. http://dx.doi.org/10.1190/geo2021-0491.1.

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Two common issues of least-squares reverse time migration (LSRTM) consist of the many iterations required to produce substantial subsurface imaging improvements and the difficulty of choosing adequate regularization strategies with optimal hyperparameters. We investigate how supervised learning can mitigate these shortcomings by solving the LSRTM problem through an iterative deep learning framework inspired by the projected gradient descent algorithm. In particular, we develop an image-to-image approach interlacing the gradient steps at each iteration with blocks of residual convolutional neur

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19

Dobramysl, Ulrich, and David Holcman. "Computational methods and diffusion theory in triangulation sensing to model neuronal navigation." Reports on Progress in Physics, September 8, 2022. http://dx.doi.org/10.1088/1361-6633/ac906b.

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Abstract Computational methods are now recognized as powerful and complementary approaches in various applied sciences such as biology. These computing methods are used to explore the gap between scales such as the one between molecular and cellular. Here we present recent progress in the development of computational approaches involving diffusion modeling, asymptotic analysis of the model partial differential equations, hybrid methods and simulations in the generic context of cell sensing and guidance via external gradients. Specifically, we highlight the reconstruction of the location of a p

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