52 research outputs found
Meshing Deforming Spacetime for Visualization and Analysis
We introduce a novel paradigm that simplifies the visualization and analysis
of data that have a spatially/temporally varying frame of reference. The
primary application driver is tokamak fusion plasma, where science variables
(e.g., density and temperature) are interpolated in a complex magnetic
field-line-following coordinate system. We also see a similar challenge in
rotational fluid mechanics, cosmology, and Lagrangian ocean analysis; such
physics implies a deforming spacetime and induces high complexity in volume
rendering, isosurfacing, and feature tracking, among various visualization
tasks. Without loss of generality, this paper proposes an algorithm to build a
simplicial complex -- a tetrahedral mesh, for the deforming 3D spacetime
derived from two 2D triangular meshes representing consecutive timesteps.
Without introducing new nodes, the resulting mesh fills the gap between 2D
meshes with tetrahedral cells while satisfying given constraints on how nodes
connect between the two input meshes. In the algorithm we first divide the
spacetime into smaller partitions to reduce complexity based on the input
geometries and constraints. We then independently search for a feasible
tessellation of each partition taking nonconvexity into consideration. We
demonstrate multiple use cases for a simplified visualization analysis scheme
with a synthetic case and fusion plasma applications
TopoSZ: Preserving Topology in Error-Bounded Lossy Compression
Existing error-bounded lossy compression techniques control the pointwise
error during compression to guarantee the integrity of the decompressed data.
However, they typically do not explicitly preserve the topological features in
data. When performing post hoc analysis with decompressed data using
topological methods, preserving topology in the compression process to obtain
topologically consistent and correct scientific insights is desirable. In this
paper, we introduce TopoSZ, an error-bounded lossy compression method that
preserves the topological features in 2D and 3D scalar fields. Specifically, we
aim to preserve the types and locations of local extrema as well as the level
set relations among critical points captured by contour trees in the
decompressed data. The main idea is to derive topological constraints from
contour-tree-induced segmentation from the data domain, and incorporate such
constraints with a customized error-controlled quantization strategy from the
classic SZ compressor.Our method allows users to control the pointwise error
and the loss of topological features during the compression process with a
global error bound and a persistence threshold
TROPHY: A Topologically Robust Physics-Informed Tracking Framework for Tropical Cyclones
Tropical cyclones (TCs) are among the most destructive weather systems.
Realistically and efficiently detecting and tracking TCs are critical for
assessing their impacts and risks. Recently, a multilevel robustness framework
has been introduced to study the critical points of time-varying vector fields.
The framework quantifies the robustness of critical points across varying
neighborhoods. By relating the multilevel robustness with critical point
tracking, the framework has demonstrated its potential in cyclone tracking. An
advantage is that it identifies cyclonic features using only 2D wind vector
fields, which is encouraging as most tracking algorithms require multiple
dynamic and thermodynamic variables at different altitudes. A disadvantage is
that the framework does not scale well computationally for datasets containing
a large number of cyclones. This paper introduces a topologically robust
physics-informed tracking framework (TROPHY) for TC tracking. The main idea is
to integrate physical knowledge of TC to drastically improve the computational
efficiency of multilevel robustness framework for large-scale climate datasets.
First, during preprocessing, we propose a physics-informed feature selection
strategy to filter 90% of critical points that are short-lived and have low
stability, thus preserving good candidates for TC tracking. Second, during
in-processing, we impose constraints during the multilevel robustness
computation to focus only on physics-informed neighborhoods of TCs. We apply
TROPHY to 30 years of 2D wind fields from reanalysis data in ERA5 and generate
a number of TC tracks. In comparison with the observed tracks, we demonstrate
that TROPHY can capture TC characteristics that are comparable to and sometimes
even better than a well-validated TC tracking algorithm that requires multiple
dynamic and thermodynamic scalar fields
Adaptively Placed Multi-Grid Scene Representation Networks for Large-Scale Data Visualization
Scene representation networks (SRNs) have been recently proposed for
compression and visualization of scientific data. However, state-of-the-art
SRNs do not adapt the allocation of available network parameters to the complex
features found in scientific data, leading to a loss in reconstruction quality.
We address this shortcoming with an adaptively placed multi-grid SRN (APMGSRN)
and propose a domain decomposition training and inference technique for
accelerated parallel training on multi-GPU systems. We also release an
open-source neural volume rendering application that allows plug-and-play
rendering with any PyTorch-based SRN. Our proposed APMGSRN architecture uses
multiple spatially adaptive feature grids that learn where to be placed within
the domain to dynamically allocate more neural network resources where error is
high in the volume, improving state-of-the-art reconstruction accuracy of SRNs
for scientific data without requiring expensive octree refining, pruning, and
traversal like previous adaptive models. In our domain decomposition approach
for representing large-scale data, we train an set of APMGSRNs in parallel on
separate bricks of the volume to reduce training time while avoiding overhead
necessary for an out-of-core solution for volumes too large to fit in GPU
memory. After training, the lightweight SRNs are used for realtime neural
volume rendering in our open-source renderer, where arbitrary view angles and
transfer functions can be explored. A copy of this paper, all code, all models
used in our experiments, and all supplemental materials and videos are
available at https://github.com/skywolf829/APMGSRN.Comment: Accepted to IEEE VIS 202
Deep Hierarchical Super-Resolution for Scientific Data Reduction and Visualization
We present an approach for hierarchical super resolution (SR) using neural
networks on an octree data representation. We train a hierarchy of neural
networks, each capable of 2x upscaling in each spatial dimension between two
levels of detail, and use these networks in tandem to facilitate large scale
factor super resolution, scaling with the number of trained networks. We
utilize these networks in a hierarchical super resolution algorithm that
upscales multiresolution data to a uniform high resolution without introducing
seam artifacts on octree node boundaries. We evaluate application of this
algorithm in a data reduction framework by dynamically downscaling input data
to an octree-based data structure to represent the multiresolution data before
compressing for additional storage reduction. We demonstrate that our approach
avoids seam artifacts common to multiresolution data formats, and show how
neural network super resolution assisted data reduction can preserve global
features better than compressors alone at the same compression ratios
TripVista: Triple Perspective Visual Trajectory Analytics and its application on microscopic traffic data at a road intersection
In this paper, we present an interactive visual analytics system, Triple Perspective Visual Trajectory Analytics (TripVista), for exploring and analyzing complex traffic trajectory data. The users are equipped with a carefully designed interface to inspect data interactively from three perspectives (spatial, temporal and multidimensional views). While most previous works, in both visualization and transportation research, focused on the macro aspects of traffic flows, we develop visualization methods to investigate and analyze microscopic traffic patterns and abnormal behaviors. In the spatial view of our system, traffic trajectories with various presentation styles are directly interactive with user brushing, together with convenient pattern exploration and selection through ring-style sliders. Improved ThemeRiver, embedded with glyphs indicating directional information, and multiple scatterplots with time as horizontal axes illustrate temporal information of the traffic flows. Our system also harnesses the power of parallel coordinates to visualize the multi-dimensional aspects of the traffic trajectory data. The above three view components are linked closely and interactively to provide access to multiple perspectives for users. Experiments show that our system is capable of effectively finding both regular and abnormal traffic flow patterns.http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000316816300021&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=8e1609b174ce4e31116a60747a720701Computer Science, Theory & MethodsEngineering, Electrical & ElectronicEICPCI-S(ISTP)2
Toward Feature-Preserving Vector Field Compression
The objective of this work is to develop error-bounded lossy compression methods to preserve topological features in 2D and 3D vector fields. Specifically, we explore the preservation of critical points in piecewise linear and bilinear vector fields. We define the preservation of critical points as, without any false positive, false negative, or false type in the decompressed data, (1) keeping each critical point in its original cell and (2) retaining the type of each critical point (e.g., saddle and attracting node). The key to our method is to adapt a vertex-wise error bound for each grid point and to compress input data together with the error bound field using a modified lossy compressor. Our compression algorithm can be also embarrassingly parallelized for large data handling and in situ processing. We benchmark our method by comparing it with existing lossy compressors in terms of false positive/negative/type rates, compression ratio, and various vector field visualizations with several scientific applications
- …