Top Challenges in the Visualization of Engineering Tensor Fields

Hlawitschka, Mario; Hotz, Ingrid; Kratz, Andrea; Marai, G. Elisabeta; Moreno, Rodrigo; Scheuermann, Gerik; Stommel, Markus; Wiebel, Alexander; Zhang, Eugene

doi:10.1007/978-3-642-54301-2_1

Cited by 3 publications

(3 citation statements)

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“…Authors who have explored tensor fields through a critical lens offer insight into the areas in need of innovation. Tensors are used extensively in numerous engineering disciplines for modeling, simulation, and analysis [Hlawitschka et al, 2014]. Engineering tensor data tends to be complex and large in volume [Carpendale, 2008], hence challenges are faced in creating effective visualization tools.…”

Section: Related Workmentioning

confidence: 99%

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Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop

Manshaei¹

2021

Preprint

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Understanding and interpreting the inherently uncertain nature of complex biological systems, as well as the time to an event in these systems, are notable challenges in the field of bioinformatics. Overcoming these challenges could potentially lead to scientific discoveries, for example paving the path for the design of new drugs to target specific diseases such as cancer, or helping to apply more effective treatment for these diseases. In general, reverse engineering of these types of biological systems using online datasets is difficult. In particular, finding a unique solution to these systems is hard due to their complexity and the small sample size of datasets. This remains an unsolved problem due to such uncertainty, and the often intractable solution space of these systems. The term"uncertainty" describes the application-based margin of significance, validity, and efficiency of inferred or predictive models in their ability to extract characteristic properties and features describing the observed state of a given biological system. In this work, uncertainties within two specific bioinformatics domains are considered, namely "gene regulatory network reconstruction" (in which gene interactions/relationships within a biological entity are inferred from gene expression data); and "cancer survivorship prediction" (in which patient survival rates are predicted based on clinical factors and treatment outcomes). One approach to reduce uncertainty is to apply different constraints that have particular relevance to each application domain. In gene network reconstruction for instance, the consideration of constraints such as sparsity, stability and modularity, can informand reduce uncertainty in the inferred reconstructions. While in cancer survival prediction, there is uncertainty in determining which clinical features (or feature aggregates) can improve associated prediction models. The inherent lack of understanding of how, why and when such constraints should be applied, however, prompts the need for a radically new approach. In this dissertation, a new approach is thus considered to aid human expert users in understanding and exploring inherent uncertainties associated with these two bioinformatics domains. Specifically, a novel set of tools is introduced and developed to assist in evidence gathering, constraint definition, and refinement of models toward the discovery of better solutions. This dissertation employs computational approaches, including convex optimization and feature selection/aggregation, in order to increase the chances of finding a unique solution. These approaches are realized through three novel interactive tools that employ tangible interaction in combination with graphical visualization to enable experts to query and manipulate the data. Tangible interaction provides physical embodiments of data and computational functions in support of learning and collaboration. Using these approaches, the dissertation demonstrates: (1) a modified stability constraint for reconstructing gene regulatory network that shows improvement in accuracy of predicted networks, (2) a novel modularity constraint (neighbor norm) for extracting available structures in the data which is validated with Laplacian eigenvalue spectrum, and (3) a hybrid method for estimating overall survival and inferring effective prognosis factors for patients with advanced prostate cancer that improves the accuracy of survival analysis.

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Section: Related Workmentioning

confidence: 99%

“…The authors contributing to this body of work, which summarizes the challenges in creating effective visualizations [Hlawitschka et al, 2014], aim at initiating a discussion among the developer community about new ways of creating solutions to the problems in existing tensor technologies.…”

Section: Related Workmentioning

confidence: 99%

Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop

Manshaei¹

2021

Preprint

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“…In the case of displaying 3D tensor field inside an object, volume rendering techniques are also required to map the volumetric data onto the screen space [3,17,35] or with the CAVE-based VR display [19]. As pointed out in [14], a technique which is intuitive to the domain-specific users is highly preferred in tensor field visualization. Although it is practical to render stress tensor fields using visualization techniques for general tensor fields, we opt to display stress tensor fields using a haptic interface, for an intuitive perception of the force-related data encoded in a stress field.…”

Section: Display Of Stress Fieldmentioning

confidence: 99%

Feel the inside: A haptic interface for navigating stress distribution inside objects

et al. 2020

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Understanding stress distributions over 3D models is a highly desired feature in many scientific and engineering fields. The stress is mathematically a second-order tensor, and it is typically visualized using either color maps, tensor glyphs, or streamlines. However, neither of these methods is physically intuitive to the end user, and they become even more awkward when dealing with the volumetric tensor field over a complicated 3D shape. In this paper, we present a virtual perception system, which leverages a multi-finger haptic interface to help users intuitively perceive 3D stress fields. Our system allows the user to navigate the interior of the 3D model freely and maps the stress tensor to the haptic rendering along the direction of the finger's trajectory. Doing so provides user a natural and straightforward understanding of the stress distribution without interacting with the parameters in the mapped visual representations. Experimental results show that our system is preferred in navigating stress fields inside an object and is applicable for different design tasks.

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Exploring Uncertainty Visualization for Degenerate Tensors in 3D Symmetric Second-Order Tensor Field Ensembles

Schmitz,

Gerrits

2024

2024 IEEE Workshop on Uncertainty Visualization: Applications, Techniques, Software, and Decision Frameworks

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Top Challenges in the Visualization of Engineering Tensor Fields

Cited by 3 publications

References 8 publications

Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop

Tangible visual analytics: the integration of tangible interactions and computational techniques for biological data visualization and modelling with experts in the loop

Feel the inside: A haptic interface for navigating stress distribution inside objects

Exploring Uncertainty Visualization for Degenerate Tensors in 3D Symmetric Second-Order Tensor Field Ensembles

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