A Multi-Branch Decoder Network Approach to Adaptive Temporal Data Selection and Reconstruction for Big Scientific Simulation Data

Zhang, Yang; Guo, Hanqi; Shang, Lanyu; Wang, Dong; Peterka, Tom

doi:10.1109/tbdata.2021.3092174

Cited by 4 publications

(3 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The third usage of autoencoders is data reduction. AE-SZ [26] and multi-branch decoder network [44] demonstrate the effectiveness of autoencoders for scientific data reduction. However, existing autoencoder-based works assume every data element is equally important without considering scientists' interest when generating the latent.…”

Section: Latent Representations In Scientific Visualizationmentioning

confidence: 97%

“…As machine learning techniques become increasingly more ubiquitous for scientific visualization and analysis, latent representations generated by autoencoders have attracted great attentions of researchers in recent years. Latent representations have been successfully demonstrated to retain essential information in the original data, and can be used for similarity analysis [11,12,18,25,28], generation of visualizations [6], synthesis of simulations [22,42,43], data reductions [26,44], and have been applied to multivariate volumetric data [28], streamlines and stream surfaces [18], isosurfaces [12], and particles [25].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

IDLat: An Importance-Driven Latent Generation Method for Scientific Data

Shen¹,

Li²,

Fan³

et al. 2022

Preprint

View full text Add to dashboard Cite

Deep learning based latent representations have been widely used for numerous scientific visualization applications such as isosurface similarity analysis, volume rendering, flow field synthesis, and data reduction, just to name a few. However, existing latent representations are mostly generated from raw data in an unsupervised manner, which makes it difficult to incorporate domain interest to control the size of the latent representations and the quality of the reconstructed data. In this paper, we present a novel importance-driven latent representation to facilitate domain-interest-guided scientific data visualization and analysis. We utilize spatial importance maps to represent various scientific interests and take them as the input to a feature transformation network to guide latent generation. We further reduced the latent size by a lossless entropy encoding algorithm trained together with the autoencoder, improving the storage and memory efficiency. We qualitatively and quantitatively evaluate the effectiveness and efficiency of latent representations generated by our method with data from multiple scientific visualization applications.

show abstract

Section: Latent Representations In Scientific Visualizationmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

IDLat: An Importance-Driven Latent Generation Method for Scientific Data

Shen¹,

Li²,

Fan³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Dimension‐reduction‐based compressors (e.g., TTHRESH [BRLP19]) reduce data dimensions by techniques such as higher‐order singular vector decomposition (HOSVD). Recently, neural networks have been widely used to reconstruct scientific data, such as autoencoders [LDZ*21, ZGS*22], super‐resolution networks [WGS*23, HZCW22], and implicit neural representations [XTS*22, LJLB21, WHW22, MLL*21, SMB*20]. Yet, most neural compressors do not offer explicit pointwise error control for scientific applications.…”

Section: Related Workmentioning

confidence: 99%

A Prediction‐Traversal Approach for Compressing Scientific Data on Unstructured Meshes with Bounded Error

Ren,

Liang,

Guo

2024

Computer Graphics Forum

View full text Add to dashboard Cite

We explore an error‐bounded lossy compression approach for reducing scientific data associated with 2D/3D unstructured meshes. While existing lossy compressors offer a high compression ratio with bounded error for regular grid data, methodologies tailored for unstructured mesh data are lacking; for example, one can compress nodal data as 1D arrays, neglecting the spatial coherency of the mesh nodes. Inspired by the SZ compressor, which predicts and quantizes values in a multidimensional array, we dynamically reorganize nodal data into sequences. Each sequence starts with a seed cell; based on a predefined traversal order, the next cell is added to the sequence if the current cell can predict and quantize the nodal data in the next cell with the given error bound. As a result, one can efficiently compress the quantized nodal data in each sequence until all mesh nodes are traversed. This paper also introduces a suite of novel error metrics, namely continuous mean squared error (CMSE) and continuous peak signal‐to‐noise ratio (CPSNR), to assess compression results for unstructured mesh data. The continuous error metrics are defined by integrating the error function on all cells, providing objective statistics across nonuniformly distributed nodes/cells in the mesh. We evaluate our methods with several scientific simulations ranging from ocean‐climate models and computational fluid dynamics simulations with both traditional and continuous error metrics. We demonstrated superior compression ratios and quality than existing lossy compressors.

show abstract