A survey on visualization of tensor field

Bi, Chen; Lü, Yang; Duan, Yulin; Shi, Yun

doi:10.1007/s12650-019-00555-8

Cited by 23 publications

(8 citation statements)

References 101 publications

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“…Further work is needed in order to extend the interpolation scheme to micro‐mechanical models and to tensorial history‐dependent state variables . Numerical testing in the context of a continuum initial boundary value problem will help to further differentiate between variations of the methods proposed here.…”

Section: Discussionmentioning

confidence: 99%

“…Becker and Wagner and Becker proposed and evaluated various schemes that are advantageous in minimizing the error induced during advection of the deformation gradient tensor field, which is an essential state variable for finite deformation hyperelastic material models. Furthermore, a vast literature of methods for the interpolation of tensors has been produced by the computer graphics and biomedical communities, see References and , and references therein for a complete overview. These efforts and the resulting methods ensure that individual state variables maintain consistency with their fundamental tensorial nature, but did not seek to ensure that combinations of state variables are in agreement with the underlying constitutive equations.…”

Section: Introductionmentioning

confidence: 99%

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A remapping scheme for history‐dependent material state variables

Versino

Mourad

Luscher

2020

Numerical Meth Engineering

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We present an interpolation scheme for use with material models involving history-dependent state variables. The interpolation scheme is based on the solution of a constrained optimization problem, to identify the smoothest monotone curve which produces an admissible solution. Unlike classical schemes used for transport equations, the proposed methodology accounts for the interdependence of the history variables and produces admissible interpolations of the material's state variables. After introducing a general solution scheme for the problem, a simplified and more computationally efficient method is derived.Finally, as a validation problem for the proposed methods, we consider the advection of internal state variables belonging to material models with highly nonlinear yield surfaces. K E Y W O R D Sadvection, arbitrary eulerian lagrangian, History variables, interpolation, remapping, state-dependent variable 1 3132

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A remapping scheme for history‐dependent material state variables

Versino

Mourad

Luscher

2020

Numerical Meth Engineering

View full text Add to dashboard Cite

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“…A survey about visualization in material science [HS17] deals with a similar application area but does not focus on tensor fields. The most recent survey on the visualization of tensor fields by Bi et al [BYDS19] is restricted to streamline and glyph-based methods focusing on DTI data.…”

Section: Scopementioning

confidence: 99%

Visualization of Tensor Fields in Mechanics

Hergl

Blecha

Kretzschmar

et al. 2021

Computer Graphics Forum

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Tensors are used to describe complex physical processes in many applications. Examples include the distribution of stresses in technical materials, acting forces during seismic events, or remodeling of biological tissues. While tensors encode such complex information mathematically precisely, the semantic interpretation of a tensor is challenging. Visualization can be beneficial here and is frequently used by domain experts. Typical strategies include the use of glyphs, color plots, lines, and isosurfaces. However, data complexity is nowadays accompanied by the sheer amount of data produced by large-scale simulations and adds another level of obstruction between user and data. Given the limitations of traditional methods, and the extra cognitive effort of simple methods, more advanced tensor field visualization approaches have been the focus of this work. This survey aims to provide an overview of recent research results with a strong application-oriented focus, targeting applications based on continuum mechanics, namely the fields of structural, bio-, and geomechanics. As such, the survey is complementing and extending previously published surveys. Its utility is twofold: (i) It serves as basis for the visualization community to get an overview of recent visualization techniques. (ii) It emphasizes and explains the necessity for further research for visualizations in this context.

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“…Leap Motion is one of the most popular gesture recognition sensors nowadays, which have two built-in cameras. Improvements in shot detection [5] have also facilitated the development of these devices. Li et al [6] proposed a dynamic gesture recognition method based on HMM and D-S evidence theory to improve the performance of the Kinect on the recognition of complex gesture movement.…”

Section: ) Gesture Interactionsmentioning

confidence: 99%

Interactive Diffusion Tensor Imaging Fiber Data Visualization Via Leap Motion

Fang

Xiao

et al. 2020

IEEE Access

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Diffusion Tensor Imaging (DTI) reveals subtle abnormalities associated with stroke, multiple sclerosis, schizophrenia and dyslexia, which has a broad application prospect in the medical field. The densely sampled 3-D DTI fiber tracts in biological specimens have high geometric, spatial and anatomical complexity. To provide users with more immersive and convenient interactions in exploring DTI fibers, we design specific interactions based on the APIs of Leap Motion. Leap Motion is a somatosensory interaction device focusing on hand tracking. We design four different interaction modes for users to analyze the data in different interaction stages and scenarios, in order to better explore the DTI fibers which users are interested in by Leap Motion gestures. They are Normal Mode, Box Basic Interaction Mode, Box Logic Operation Mode, and Cluster Exploration Mode. Users can conduct tradition manipulations over the whole DTI fiber data in Normal Mode. Boxes are employed to filter out uninteresting tracts in Box Basic Interaction Mode, and expression-based queries are further designed to get logic set operations based on multiple boxes in Box Logic Operation Mode, e.g., the intersection, union and complement of boxes. Complex logic combination queries are allowed to perform to reduce visual clutter and help users explore DTI fibers more precisely. In Cluster Exploration Mode, DTI fibers can be classified by clustering them into spatially and anatomically related tracts and then different clusters can be explored individually by designed gestures. Compared with the explorations through traditional input devices, the evaluation tests show that the proposed approach is more intuitive and efficient in 3-D explorations and provides an immersive experience for users to explore the DTI fiber data. INDEX TERMS Data visualization, data analysis, graphical user interfaces, human computer interaction.

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A survey on visualization of tensor field

Cited by 23 publications

References 101 publications

A remapping scheme for history‐dependent material state variables

A remapping scheme for history‐dependent material state variables

Visualization of Tensor Fields in Mechanics

Interactive Diffusion Tensor Imaging Fiber Data Visualization Via Leap Motion

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