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Call for Papers: A Special Issue of <span style="color: rgb(0, 0, 255);">Journal of Flow Visualization and Image Processing (JFVIP)</span>
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<div><span style="color: rgb(0, 0, 255);">Numerical Flow Visualization: From the Perspectives of Computer Graphics and Machine Learning</span>
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<div>(<span style="color: rgb(0, 0, 255);">https://www.begellhouse.com/journals/flow-visualization-and-image-processing.html</span><span style="color: rgb(0, 0, 255);">)</span></div>
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<div>Due of Submission: August 31, 2021</div>
<div>Paper length (recommended maximum): 5000 words in text; 4-6 figures</div>
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<div>Founded in the early 1990s for quarterly publishing, JFVIP has a well-established readership base, while authors see it primarily as a journal of flow imaging from the measurement viewpoint. Image formation from experiments and simulations is a fundamental
component of the journal. Flow data arising from numerical simulation encodes a wealth of information that needs to be extracted and displayed so as to gain insight into the pattern. In this respect, it is an increasingly important task to process, represent,
visualize, and analyze flow data. This paradigm maps an array of discrete data values either to geometric elements followed by graphics rendering or directly to pixels. Coupled with this visualization pipeline is an emerging pre-processing stage of great promise
that unleashes the power of artificial intelligence, particularly machine learning, to perform feature detection and pattern recognition. In light of the importance of computer graphics and machine learning to data analysis, we bring in numerical flow visualization
to serve our core purpose of extracting and exploiting information towards scientific discovery.</div>
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<div>Numerical flow visualization is focused on velocity vector data (accompanied by scalar variables such as pressure and temperature) resulting from Computational Fluid Dynamics (CFD) simulation of complex systems, processes, or phenomena, e.g., wind/air
flows, ocean currents, combustion, electromagnetic fields, and blood flows. Also addressed are velocity vector data obtained from laboratory-scale experiments or imaging modalities, e.g., Diffusion Tensor Imaging (DTI) of cardiovascular blood flows and neural
fibers in the human body. As flow data range from planar to surface and further to volumetric domains, from steady to unsteady cases, from structured to unstructured grids, and from Megabyte to Terabyte and even to Exabyte scales, these factors pose daunting
challenges to representation, display, exploration, and analysis of complex structures. Algorithm research and system development revolving around these topics, among others, will contribute to advances in numerical flow visualization.</div>
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<div>This special issue is an initiative for significantly expanding the scope of the journal to include computer graphics and machine learning so that a variety of numerical methodologies are utilized to achieve effective flow visualization. Original submissions
and review articles are both welcome. </div>
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<div>Guest Editors</div>
<div>Zhanping Liu (z<span style="color: rgb(134, 17, 6);"><b>1</b></span>Liu@odu.edu), Old Dominion University</div>
K. Muralidhar (Editor-in-Chief of JFVIP, kmurli@iitk.ac.in), IIT Kanpur (India)</div>
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<div>=============================================<br>
Dr. Zhanping Liu / <a href="http://www.zhanpingliu.org/" target="_blank">www.zhanpingliu.org</a>
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