Robust Reference Frame Extraction from Unsteady 2D Vector Fields with Convolutional Neural Networks

Kim, Byungsoo; Günther, Tobias

doi:10.1111/cgf.13689

Cited by 39 publications

(20 citation statements)

References 42 publications

(75 reference statements)

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“…We also refer to their research tasks when categorizing the surveyed papers in the respective tables according to learning type, network architecture, loss function, and evaluation metric. The description [51] TSR-TVD TVCG Han and Wang [50] SSR-TVD TVCG Han et al [55] STNet TVCG Wurster et al [167] arXiv Guo et al [47] SSR-VFD PVIS Jakob et al [76] TVCG Sahoo and Berger [126] IA-VFS EVIS An et al [2] STSRNet CG&A Han and Wang [53] TSR-VFD C&G Xie et al [168] tempoGAN TOG Werhahn et al [162] CGIT Wang et al [156] DeepOrganNet TVCG Lu et al [109] neurcomp CGF Weiss et al [160] fV-SRN arXiv Shi et al [131] GNN-Surrogate TVCG Han and Wang [54] VCNet VI Liu et al [106] JOV Han et al [49] CG&A Gu et al [45] VFR-UFD CG&A Han et al [56] V2V TVCG Gu et al [46] Scalar2Vec PVIS Kim et al [84] Deep Fluids CGF Chu et al [27] TOG Wiewel et al [163] LSP CGF Wiewel et al [164] LSS CGF Berger et al [12] TVCG Hong et al [70] DNN-VolVis PVIS He et al [63] InSituNet TVCG Weiss et al [159] TVCG Weiss et al [161] TVCG Weiss and Navab [158] DeepDVR arXiv He et al [62] CECAV-DNN VI Tkachev et al [143] TVCG Hong et al [71] PVIS Kim and Günther [85] CGF Han et al [57] arXiv Yang et al [169] JOV Shi and Tao [130] TIST Engel and Ropinski …”

Section: Dl4scivis Workmentioning

confidence: 99%

DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization

Wang¹,

Han²

2022

Preprint

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Since 2016, we have witnessed the tremendous growth of artificial intelligence+visualization (AI+VIS) research. However, existing survey papers on AI+VIS focus on visual analytics and information visualization, not scientific visualization (SciVis). In this paper, we survey related deep learning (DL) works in SciVis, specifically in the direction of DL4SciVis: designing DL solutions for solving SciVis problems. To stay focused, we primarily consider works that handle scalar and vector field data but exclude mesh data. We classify and discuss these works along six dimensions: domain setting, research task, learning type, network architecture, loss function, and evaluation metric. The paper concludes with a discussion of the remaining gaps to fill along the discussed dimensions and the grand challenges we need to tackle as a community. This state-of-the-art survey guides SciVis researchers in gaining an overview of this emerging topic and points out future directions to grow this research.

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Section: Dl4scivis Workmentioning

confidence: 99%

DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization

Wang¹,

Han²

2022

Preprint

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“…For example, it has been widely used to detect flow field features such as eddies and vortices ( [5,12,14,37,39,58,61,63]). [33] utilized the convolutional neural networks (CNNs) to extract a robust frame of reference for unsteady two-dimensional (2D) vector fields. [28] used the long short-term memory (LSTM) to improve data access patterns for improved computational performance during distributed memory particle advection.…”

Section: Flow Visualization Using Machine Learningmentioning

confidence: 99%

Exploratory Lagrangian-Based Particle Tracing Using Deep Learning

Han,

Sane,

Johnson

2021

Preprint

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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.

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“…Hence, we assess the difference with an L 1 norm that is combined with a gradient loss to not only penalize differences in the values but also in the derivatives, which aids in the reconstruction of higherfrequency details. We refer to Kim and Günther (2019) and for a discussion of the norms and weights of the gradient loss.…”

Section: Training Procedures and Lossmentioning

confidence: 99%

Spatio-Temporal Downscaling of Climate Data Using Convolutional and Error-Predicting Neural Networks

2021

Self Cite

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Numerical weather and climate simulations nowadays produce terabytes of data, and the data volume continues to increase rapidly since an increase in resolution greatly benefits the simulation of weather and climate. In practice, however, data is often available at lower resolution only, for which there are many practical reasons, such as data coarsening to meet memory constraints, limited computational resources, favoring multiple low-resolution ensemble simulations over few high-resolution simulations, as well as limits of sensing instruments in observations. In order to enable a more insightful analysis, we investigate the capabilities of neural networks to reconstruct high-resolution data from given low-resolution simulations. For this, we phrase the data reconstruction as a super-resolution problem from multiple data sources, tailored toward meteorological and climatological data. We therefore investigate supervised machine learning using multiple deep convolutional neural network architectures to test the limits of data reconstruction for various spatial and temporal resolutions, low-frequent and high-frequent input data, and the generalization to numerical and observed data. Once such downscaling networks are trained, they serve two purposes: First, legacy low-resolution simulations can be downscaled to reconstruct high-resolution detail. Second, past observations that have been taken at lower resolutions can be increased to higher resolutions, opening new analysis possibilities. For the downscaling of high-frequent fields like precipitation, we show that error-predicting networks are far less suitable than deconvolutional neural networks due to the poor learning performance. We demonstrate that deep convolutional downscaling has the potential to become a building block of modern weather and climate analysis in both research and operational forecasting, and show that the ideal choice of the network architecture depends on the type of data to predict, i.e., there is no single best architecture for all variables.

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Robust Reference Frame Extraction from Unsteady 2D Vector Fields with Convolutional Neural Networks

Cited by 39 publications

References 42 publications

DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization

DL4SciVis: A State-of-the-Art Survey on Deep Learning for Scientific Visualization

Exploratory Lagrangian-Based Particle Tracing Using Deep Learning

Spatio-Temporal Downscaling of Climate Data Using Convolutional and Error-Predicting Neural Networks

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