A Survey of Seed Placement and Streamline Selection Techniques

Sane, Sudhanshu; Bujack, Roxana; Garth, Christoph; Childs, Hank

doi:10.1111/cgf.14036

Cited by 25 publications

(16 citation statements)

References 111 publications

(122 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…[38] employed the support vector machine (SVM) to segment streamlines based on user-identified features. For the widely studied task of selecting a representative set of particle trajectories ( [50]), recent state-of-the-art techniques by [21] and [35] have used deep-learning-based clustering approaches. Further, modern techniques to reconstruct steady state vector fields using a set of streamlines employ machine learning ( [22,48]).…”

Section: Flow Visualization Using Machine Learningmentioning

confidence: 99%

Exploratory Lagrangian-Based Particle Tracing Using Deep Learning

Han,

Sane,

Johnson

2021

Preprint

Self Cite

View full text Add to dashboard Cite

Time-varying vector fields produced by computational fluid dynamics simulations are often prohibitively large and pose challenges for accurate interactive analysis and exploration. To address these challenges, reduced Lagrangian representations have been increasingly researched as a means to improve scientific time-varying vector field exploration capabilities. This paper presents a novel deep neural network-based particle tracing method to explore time-varying vector fields represented by Lagrangian flow maps. In our workflow, in situ processing is first utilized to extract Lagrangian flow maps, and deep neural networks then use the extracted data to learn flow field behavior. Using a trained model to predict new particle trajectories offers a fixed small memory footprint and fast inference. To demonstrate and evaluate the proposed method, we perform an in-depth study of performance using a well-known analytical data set, the Double Gyre. Our study considers two flow map extraction strategies as well as the impact of the number of training samples and integration durations on efficacy, evaluates multiple sampling options for training and testing, and informs hyperparameter settings. Overall, we find our method requires a fixed memory footprint of 10.5 MB to encode a Lagrangian representation of a time-varying vector field while maintaining accuracy. For post hoc analysis, loading the trained model costs only two seconds, significantly reducing the burden of I/O when reading data for visualization. Moreover, our parallel implementation can infer one hundred locations for each of two thousand new pathlines across the entire temporal resolution in 1.3 seconds using one NVIDIA Titan RTX GPU.

show abstract

Section: Flow Visualization Using Machine Learningmentioning

confidence: 99%

Exploratory Lagrangian-Based Particle Tracing Using Deep Learning

Han,

Sane,

Johnson

2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Recently, a survey by Sane et al [24] provided a thorough overview of this research field. They divided the techniques into three different categories -density-based, feature-based, and similarity-based methods -for automated techniques and two categories -interactive tools and domain information -for manual techniques.…”

Section: A Seed Placement and Streamline Selectionmentioning

confidence: 99%

Deep Regression Network-Assisted Efficient Streamline Generation Method

Lee

Park

2021

IEEE Access

View full text Add to dashboard Cite

Streamlining is one of the most frequently utilized visualization methods to analyze the flow structure of computational fluid dynamics (CFD) data. However, it is challenging to find a set of streamlines showing the most prominent flow across the entire flow field due to the heavy computation time required to generate bundles of streamlines. In this paper, we propose an efficient streamline generation method that removes several seed candidates that are predicted as less important using a 3D U-net based regression model. We employ 3D line integral convolution (LIC) volumes that depict the entire flow field for training data of the proposed learning model and evaluate our method using a real-world CFD data set. We find using our model that we can obtain quality of visualization results comparable to that of the ground truth even when more than 90% of the seed candidates are truncated while operating 6.6∼17.1 times faster than the competing method.INDEX TERMS deep regression network, dilated convolutional neural network, flow visualization, line integral convolution, streamline

show abstract

“…Given a large set of curves, which densely cover a domain, the goal of curve selection is to choose a few representatives that convey the main features of the data [SBGC20]. In general, there are two classes of methods: importance‐based and clustering‐based selection.…”

Section: Related Workmentioning

confidence: 99%

Curve Complexity Heuristic KD‐trees for Neighborhood‐based Exploration of 3D Curves

Cheng

Isenberg

et al. 2021

Computer Graphics Forum

View full text Add to dashboard Cite

We introduce the curve complexity heuristic (CCH), a KD-tree construction strategy for 3D curves, which enables interactive exploration of neighborhoods in dense and large line datasets. It can be applied to searches of k-nearest curves (KNC) as well as radius-nearest curves (RNC). The CCH KD-tree construction consists of two steps: (i) 3D curve decomposition that takes into account curve complexity and (ii) KD-tree construction, which involves a novel splitting and early termination strategy. The obtained KD-tree allows us to improve the speed of existing neighborhood search approaches by at least an order of magnitude (i. e., 28× for KNC and 12× for RNC with 98% accuracy) by considering local curve complexity. We validate this performance with a quantitative evaluation of the quality of search results and computation time. Also, we demonstrate the usefulness of our approach for supporting various applications such as interactive line queries, line opacity optimization, and line abstraction. CCS Concepts• Human-centered computing → Scientific visualization; • Theory of computation → Nearest neighbor algorithms;

show abstract

A Survey of Seed Placement and Streamline Selection Techniques

Cited by 25 publications

References 111 publications

Exploratory Lagrangian-Based Particle Tracing Using Deep Learning

Exploratory Lagrangian-Based Particle Tracing Using Deep Learning

Deep Regression Network-Assisted Efficient Streamline Generation Method

Curve Complexity Heuristic KD‐trees for Neighborhood‐based Exploration of 3D Curves

Contact Info

Product

Resources

About