Interpreting Galilean Invariant Vector Field Analysis via Extended Robustness

Wang, Bei; Bujack, Roxana; Rosen, Paul; Škraba, Primož; Bhatia, Harsh; Hagen, Hans

doi:10.1007/978-3-030-43036-8_14

Cited by 3 publications

(3 citation statements)

References 23 publications

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“…Lately, Wang et al. [WBR*17] further extended the classic definition of robustness to a Galilean invariant robustness framework that quantifies the stability of critical points across different frames of reference. The notion of robustness was further extended to study the stability of degenerate points in tensor fields [WH17, JWH19].…”

Section: Related Workmentioning

confidence: 99%

“…The robustness of a critical point is defined to be the minimum amount of perturbation to the vector field necessary to cancel it. Robustness has been shown to be useful in feature extraction [WBR*17] and simplification [SWCR14, SWCR15, SRW*16] of vector field data. In particular, Skraba and Wang inferred correspondences between critical points based on their closeness in stability, measured by robustness, instead of just distance proximity within the domain [SW14].…”

Section: Introductionmentioning

confidence: 99%

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Multilevel Robustness for 2D Vector Field Feature Tracking, Selection and Comparison

Yan

Ullrich

Roekel³

et al. 2023

Computer Graphics Forum

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Critical point tracking is a core topic in scientific visualization for understanding the dynamic behaviour of time‐varying vector field data. The topological notion of robustness has been introduced recently to quantify the structural stability of critical points, that is, the robustness of a critical point is the minimum amount of perturbation to the vector field necessary to cancel it. A theoretical basis has been established previously that relates critical point tracking with the notion of robustness, in particular, critical points could be tracked based on their closeness in stability, measured by robustness, instead of just distance proximity within the domain. However, in practice, the computation of classic robustness may produce artifacts when a critical point is close to the boundary of the domain; thus, we do not have a complete picture of the vector field behaviour within its local neighbourhood. To alleviate these issues, we introduce a multilevel robustness framework for the study of 2D time‐varying vector fields. We compute the robustness of critical points across varying neighbourhoods to capture the multiscale nature of the data and to mitigate the boundary effect suffered by the classic robustness computation. We demonstrate via experiments that such a new notion of robustness can be combined seamlessly with existing feature tracking algorithms to improve the visual interpretability of vector fields in terms of feature tracking, selection and comparison for large‐scale scientific simulations. We observe, for the first time, that the minimum multilevel robustness is highly correlated with physical quantities used by domain scientists in studying a real‐world tropical cyclone dataset. Such an observation helps to increase the physical interpretability of robustness.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multilevel Robustness for 2D Vector Field Feature Tracking, Selection and Comparison

Yan

Ullrich

Roekel³

et al. 2023

Computer Graphics Forum

Self Cite

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“…Figure 68 shows an adaptive sampling technique based on importance to preserve features in the data [210,211,212]. Moments-based pattern detection can be used to find rotation-invariant patterns [213,214,215]. For algorithms, we have completed the initial R&D phase for our four mature algorithms (STDA04-5).…”

Section: Solution Strategymentioning

confidence: 99%

ECP Software Technology Capability Assessment Report

Heroux

Carter

Thakur

et al. 2020

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The Exascale Computing Project (ECP) Software Technology (ST) Focus Area is responsible for developing critical software capabilities that will enable successful execution of ECP applications, and for providing key components of a productive and sustainable Exascale computing ecosystem that will position the US Department of Energy (DOE) and the broader high performance (HPC) community with a firm foundation for future extreme-scale computing capabilities.This ECP ST Capability Assessment Report (CAR) provides an overview and assessment of current ECP ST capabilities and activities, giving stakeholders and the broader HPC community information that can be used to assess ECP ST progress and plan their own efforts accordingly. ECP ST leaders commit to updating this document on regular basis (every six to 12 months). Highlights from this version of the report are presented here.What is new in CAR V2.0: CAR V2.0 contains the following updates relative to CAR V1.5.• We introduce the FY20-23 project structure. ECP ST now consists of 6 (up from 5) level-3 (L3) technical areas, introducing the NNSA ST L3 area, which brings into one L3 the ECP open source development efforts at NNSA labs that are of particular importance to the rest of ECP. The number of ECP ST level-4 (L4) subprojects has been reduced from 55 to 33. The strategic aggregation of projects into fewer and larger units enables us to better manage L4 subprojects consistently as a portfolio. See Section 1.2.• We describe new and enhanced project management processes and resources including our iterative planning process, new KPP-3 capability integration process, a product dictionary and dependency management database. These new project features and related dashboards enable more insight and better information to effectively manage efforts across ECP. See Section 2.• The two-page summaries of each ECP L4 projects have been updated to reflect recent progress and next steps. See Section 4.• The Extreme-scale Scientific Software Stack (E4S) is further described. The third release, which is also the first major public release Version 1.0, was November 18, 2019. E4S is the primary integration and delivery vehicle for ECP ST capabilities. See Section 2.1.1.• The ECP ST SDK effort has further refined its groupings. See Section 2.1.2.The Exascale Computing Project Software Technology (ECP ST) focus area represents the key bridge between Exascale systems and the scientists developing applications that will run on those platforms. ECP ST efforts contribute to 70 software products (Section 2.1.3) in six technical areas (Table 1). Since the publishing of CAR V1.5, we have introduced a product dictionary of official product names, which enables more rigorous mapping of ECP ST deliverables to stakeholders (Section 2.1.4).Programming Models & Runtimes: In addition to developing key enhancements to MPI and OpenMP for scalable systems with accelerated node architectures, we are working on performance portability layers (Kokkos and RAJA) and participating in OpenMP and OpenACC so...

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