$$\mathrm{ND}^2\mathrm{AV}$$ ND 2 AV : N-dimensional data analysis and visualization analysis for the National Ignition Campaign

Bremer, Peer-Timo; Maljovec, Dan; Saha, Avishek; Wang, Bei; Gaffney, Jim; Spears, B. K.; Pascucci, Valerio

doi:10.1007/s00791-015-0241-3

Cited by 12 publications

(3 citation statements)

References 41 publications

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“…Conversely, a lower threshold with more details leads to better alignment between the MetroSets and the shape of the AR region, but may cause visual clutter. An appropriate persistence threshold is typically selected using a persistence graph [BMS*15], where a plateau in the persistence graph indicates a stable range of scales to separate noise from features. Automating the selection process could be an interesting future direction.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Topological Characterization and Uncertainty Visualization of Atmospheric Rivers

Lan,

Gamelin,

Yan

et al. 2024

Computer Graphics Forum

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Atmospheric rivers (ARs) are long, narrow regions of water vapor in the Earth's atmosphere that transport heat and moisture from the tropics to the mid‐latitudes. ARs are often associated with extreme weather events in North America and contribute significantly to water supply and flood risk. However, characterizing ARs has been a major challenge due to the lack of a universal definition and their structural variations. Existing AR detection tools (ARDTs) produce distinct AR boundaries for the same event, making the risk assessment of ARs a difficult task. Understanding these uncertainties is crucial to improving the predictability of AR impacts, including their landfall areas and associated precipitation, which could cause catastrophic flooding and landslides over the coastal regions. In this work, we develop an uncertainty visualization framework that captures boundary and interior uncertainties, i.e., structural variations, of an ensemble of ARs that arise from a set of ARDTs. We first provide a statistical overview of the AR boundaries using the contour boxplots of Whitaker et al. that highlight the structural variations of AR boundaries based on their nesting relationships. We then introduce the topological skeletons of ARs based on Morse complexes that characterize the interior variation of an ensemble of ARs. We propose an uncertainty visualization of these topological skeletons, inspired by MetroSets of Jacobson et al. that emphasizes the agreements and disagreements across the ensemble members. Through case studies and expert feedback, we demonstrate that the two approaches complement each other, and together they could facilitate an effective comparative analysis process and provide a more confident outlook on an AR's shape, area, and onshore impact.

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Section: Conclusion and Discussionmentioning

confidence: 99%

Topological Characterization and Uncertainty Visualization of Atmospheric Rivers

Lan,

Gamelin,

Yan

et al. 2024

Computer Graphics Forum

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“…In particular, to guide the selection of the persistence threshold, we employ a set of persistence graphs, each of which represent the number of persistence pairs as a function of persistence [62]. The shape of the persistence graph, in particular, a plateau, indicates a stable range of scales to separate noise from signals in the persistence graph [63], [64]. We demonstrate such a simplification process in Fig.…”

Section: Detect and Diagnose Structural Transitionsmentioning

confidence: 99%

Geometry-Aware Merge Tree Comparisons for Time-Varying Data with Interleaving Distances

Yang¹,

Masood²,

Rasheed³

et al. 2021

Preprint

Self Cite

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Merge trees, a type of topological descriptors, serve to identify and summarize the topological characteristics associated with scalar fields. They present a great potential for the analysis and visualization of time-varying data. First, they give compressed and topology-preserving representations of data instances. Second, their comparisons provide a basis for studying the relations among data instances, such as their distributions, clusters, outliers, and periodicities. A number of comparative measures have been developed for merge trees. However, these measures are often computationally expensive since they implicitly consider all possible correspondences between critical points of the merge trees. In this paper, we perform geometry-aware comparisons of merge trees using labeled interleaving distances. The main idea is to decouple the computation of a comparative measure into two steps: a labeling step that generates a correspondence between the critical points of two merge trees, and a comparison step that computes distances between a pair of labeled merge trees by encoding them as matrices. We show that our approach is general, computationally efficient, and practically useful. Our general framework makes it possible to integrate geometric information of the data domain in the labeling process. At the same time, it reduces the computational complexity since not all possible correspondences have to be considered. We demonstrate via experiments that such geometry-aware merge tree comparisons help to detect transitions, clusters, and periodicities of time-varying datasets, as well as to diagnose and highlight the topological changes between adjacent data instances.

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“…The work along with utilizing state of the art tools such as machine learning is being applied to this problem for ICF. Large ensembles of simulations with machine learning techniques are being used to determine the principle metrics for ICF implosion performance [95][96][97][98][99]. Large ensembles of simulation output and experimental data are being coupled with machine learning techniques to propagate and incorporate uncertainty in predictions to new regimes, assessing and evaluating competing hypotheses for performance in the face of statistical, experimental and numerical uncertainties.…”

Section: Scalingmentioning

confidence: 99%

Progress of indirect drive inertial confinement fusion in the United States

Kline¹,

Batha²,

Benedetti³

et al. 2019

Nucl. Fusion

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Indirect drive converts high power laser light into x-rays using small high-Z cavities called hohlraums. X-rays generated at the hohlraum walls drive a capsule filled with deuterium–tritium (DT) fuel to fusion conditions. Recent experiments have produced fusion yields exceeding 50 kJ where alpha heating provides ~3× increase in yield over PdV work. Closing the gaps toward ignition is challenging, requiring optimization of the target/implosions and the laser to extract maximum energy. The US program has a three-pronged approach to maximize target performance, each closing some portion of the gap. The first item is optimizing the hohlraum to couple more energy to the capsule while maintaining symmetry control. Novel hohlraum designs are being pursued that enable a larger capsule to be driven symmetrically to both reduce 3D effects and increase energy coupled to the capsule. The second issue being addressed is capsule stability. Seeding of instabilities by the hardware used to mount the capsule and fill it with DT fuel remains a concern. Work reducing the impact of the DT fill tubes and novel capsule mounts is being pursed to reduce the effect of mix on the capsule implosions. There is also growing evidence native capsule seeds such as a micro-structure may be playing a role on limiting capsule performance and dedicated experiments are being developed to better understand the phenomenon. The last area of emphasis is the laser. As technology progresses and understanding of laser damage/mitigation advances, increasing the laser energy seems possible. This would increase the amount of energy available to couple to the capsule, and allow larger capsules, potentially increasing the hot spot pressure and confinement time. The combination of each of these focus areas has the potential to produce conditions to initiate thermo-nuclear ignition.

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$$\mathrm{ND}^2\mathrm{AV}$$ ND 2 AV : N-dimensional data analysis and visualization analysis for the National Ignition Campaign

Cited by 12 publications

References 41 publications

Topological Characterization and Uncertainty Visualization of Atmospheric Rivers

Topological Characterization and Uncertainty Visualization of Atmospheric Rivers

Geometry-Aware Merge Tree Comparisons for Time-Varying Data with Interleaving Distances

Progress of indirect drive inertial confinement fusion in the United States

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