Making Sense of Scientific Simulation Ensembles With Semantic Interaction

In the study of complex physical systems, scientists use simulations to study the effects of different models and parameters. Seeking to understand the influence and relationships among multiple dimensions, they typically run many simulations and vary the initial conditions in what are known as ‘ensembles’. Ensembles are then a number of runs that are each multi-dimensional and multi-variate. In order to understand the connections between simulation parameters and patterns in the output data, we have been developing an approach to the visual analysis of scientific data that merges human expertise and intuition with machine learning and statistics. Our approach is manifested in a new visualization tool, GLEE (Graphically-Linked Ensemble Explorer), that allows scientists to explore, search, filter and make sense of their ensembles. GLEE uses visualization and semantic interaction (SI) techniques to enable scientists to find similarities and differences between runs, find correlation(s) between different parameters and explore relations and correlations across and between different runs and parameters. Our approach supports scientists in selecting interesting subsets of runs in order to investigate and summarize the factors and statistics that show variations and consistencies across different runs. In this paper, we evaluate our tool with experts to understand its strengths and weaknesses for optimization and inverse problems.     

高速网络环境中的科学计算可视化

可视化技术已经极大地提高了研究人员分析、理解数据的能力，科学计算可视化和高速网络的紧密结合为超级计算提供了一种有效环境。本文主要介绍科学计算可视化在网络环境中所面临的一些问题，提出一种解决负载分配问题的有效方法，最后，总结未来超级计算所需可视化环境的研究工作。     

Visualization of Multi-Variate Scientific Data

In this state-of-the-art report we discuss relevant research works related to the visualization of complex, multi-variate data. We discuss how different techniques take effect at specific stages of the visualization pipeline and how they apply to multi-variate data sets being composed of scalars, vectors and tensors. We also provide a categorization of these techniques with the aim for a better overview of related approaches. Based on this classification we highlight combinable and hybrid approaches and focus on techniques that potentially lead towards new directions in visualization research. In the second part of this paper we take a look at recent techniques that are useful for the visualization of complex data sets either because they are general purpose or because they can be adapted to specific problems.     

基于样条模型插值的方法在科学可视化上的应用

插值方法和插值基函数的选择是可视化技术的一个关键问题。在规则数据场中，本文提出了一个基于样条模型插值来重建三维结构的方法。该模型以样条基函数为背景，其函数系数则由周围采样点数据重复迭代产生。实验表明该方法的逼近效果明显优于传统的线性插值拟合得出的图像质量。     

科学计算可视化

科学计算可视化(V_isc)是正在兴起的崭新技术领域。V_isc把数据信息转换成可视信息,是分析与解释大体积数据的有力工具。本文阐述了V_isc技术的若干重要方面,指出研制可视化应用开发环境VadE的主要目标,即与学科无关性和可视驾驭计算功能。     

大规模科学可视化的数据流网络优化分析

对大规模科学数据的分析与理解最终要依赖于可视化手段来完成,数据的存储与组织方式是影响可视化效率的关键因素,特别是集成大量可视化与数据分析功能的大规模并行分布式可视化与数据分析系统、可视化流程及数据流网络中所需要的优化手段对数据的存储与组织提出更高的要求.通过分析针对大规模数据可视化所采用的方式方法总结对数据及相关信息的存储与组织需求,借助高效的数据组织方式加快科学可视化过程.     

变电站三维模型可视化交互研究

随着电网信息化建设的加快,通过对变电站主设备和辅助设备进行三维场景建模,实现变电站场景的数字化还原,并根据变电站内设备信息将三维节点与数据库点进行逻辑关联,将变电站监控推向数字化、可视化、智能化的全新高度。本文旨在研究一种通用的三维交互方法,通过规范化三维模型的动态节点命名的方法,实现三维模型和三维程序之间的交互信息的自动化提取和控制,从而对变电站主设备监测数据、辅助设备监测数据等进行了全面监视,构建智慧变电站三维可视化交互平台,更加高效直观地全面监视变电站,增强设备的管控力。     

移动增强现实浏览器的信息可视化和交互式设计

为探究解决增强现实浏览器人因问题的方法,通过对移动增强现实环境中的人因问题进行分析,提出一个新型设计流程,并在实际设计中论证其有效性.首先对移动增强现实浏览器进行用户需求分析,并将需求转化为任务;然后将抽象信息可视化为数据视图,并采用概览与细节方法对构成视图的视觉元素进行布局;再使用渐进式交互法组织离散数据视图,生成交互界面;最终通过对浏览器原型的迭代设计解决评测过程中发现的人为设计错误.可用性测试结果表明,采用该方法设计的增强现实交互系统增进了用户对目标的认知,改善了用户体验.     

用MATLAB实现电磁波与目标作用的动态可视化

给出了一种实现电磁波与目标相互作用现象可视化的方法。首先用时域有限差分法在数值上模拟电磁波与目标相互作用过程,获取电磁场数据,然后利用MATLAB软件的科学计算可视化功能,将原始数据转换为动态图像。从而能够观察到电磁波传播、穿透、散射和吸收等现象,为直观地了解电磁波与目标相互作用的过程提供了一个有效的手段。     

Using VisTrails and Provenance for Teaching Scientific Visualization

Over the last 20 years, visualization courses have been developed and offered at universities around the world. Many of these courses use established visualization libraries and tools (e.g. VTK, ParaView, AVS, VisIt) as a way to provide students a hands‐on experience, allowing them to prototype and explore different visualization techniques. In this paper, we describe our experiences using VisTrails as a platform to teach scientific visualization. VisTrails is an open‐source system that was designed to support exploratory computational tasks such as visualization and data analysis. Unlike previous scientific workflow and visualization systems, VisTrails provides a comprehensive provenance management infrastructure. We discuss how different features of the system, and in particular, the provenance information have changed the dynamics of the Scientific Visualization course we offer at the University of Utah. We also describe our initial attempts at using the provenance information to better assess our teaching techniques and student performance.     

Recent Advances in Volume Visualization

In the past few years, there have been key advances in the three main approaches to the visualization of volumetric data: isosurfacing, slicing and volume rendering, which together make up the field of volume visualization.
In this survey paper we set the scene by describing the fundamental techniques for each of these approaches, using this to motivate the range of advances which have evolved over the past few years.
In isosurfacing, we see how the original marching cubes algorithm has matured, with improvements in robustness, topological consistency, accuracy and performance. In the performance area, we look in detail at pre-processing steps which help identify data which contributes to the particular isosurface required. In slicing too, there are performance gains from identifying active cells quickly.
In volume rendering, we describe the two main approaches of ray casting and projection. Both approaches have evolved technically over the past decade, and the holy grail of real-time volume rendering has arguably been reached.
The aim of this review paper is to pull these developments together in a coherent review of recent advances in volume visualization.     

Graphs in Scientific Visualization: A Survey

Graphs represent general node‐link diagrams and have long been utilized in scientific visualization for data organization and management. However, using graphs as a visual representation and interface for navigating and exploring scientific data sets has a much shorter history, yet the amount of work along this direction is clearly on the rise in recent years. In this paper, we take a holistic perspective and survey graph‐based representations and techniques for scientific visualization. Specifically, we classify these representations and techniques into four categories, namely partition‐wise, relationship‐wise, structure‐wise and provenance‐wise. We survey related publications in each category, explaining the roles of graphs in related work and highlighting their similarities and differences. At the end, we reexamine these related publications following the graph‐based visualization pipeline. We also point out research trends and remaining challenges in graph‐based representations and techniques for scientific visualization.     

State of the Art of Molecular Visualization in Immersive Virtual Environments

Visualization plays a crucial role in molecular and structural biology. It has been successfully applied to a variety of tasks, including structural analysis and interactive drug design. While some of the challenges in this area can be overcome with more advanced visualization and interaction techniques, others are challenging primarily due to the limitations of the hardware devices used to interact with the visualized content. Consequently, visualization researchers are increasingly trying to take advantage of new technologies to facilitate the work of domain scientists. Some typical problems associated with classic 2D interfaces, such as regular desktop computers, are a lack of natural spatial understanding and interaction, and a limited field of view. These problems could be solved by immersive virtual environments and corresponding hardware, such as virtual reality head-mounted displays. Thus, researchers are investigating the potential of immersive virtual environments in the field of molecular visualization. There is already a body of work ranging from educational approaches to protein visualization to applications for collaborative drug design. This review focuses on molecular visualization in immersive virtual environments as a whole, aiming to cover this area comprehensively. We divide the existing papers into different groups based on their application areas, and types of tasks performed. Furthermore, we also include a list of available software tools. We conclude the report with a discussion of potential future research on molecular visualization in immersive environments.     

基于Flask和ECharts的科研数据可视化系统

The scientific research ability is one of the important factors which affect the strength of colleges and universities. Throughdata analysis and visualization, the scientific research level of universities and teachers can be displayed intuitively. In order to betterreflect the scientific research level of colleges and universities, this paper designs and implements the scientific research data visual-ization system. This system uses the PyCharm development platform, uses Bootstrap for page layout to form the overall structure, us-es SqlAlchemy to operate the database in the background, and transmits data through the Flask Web framework. According to thecharacteristics of data visualization, different types of data charts are drawn by ECharts technology, and the synchronous display ofdata is completed withAjax asynchronous request.     

宇宙数值模拟中可视化方法概述

数值模拟是宇宙学中重要的研究方法,可以帮助科学家了解宇宙演化过程和验证理论模型.可视化是分析模拟数据最有效的手段之一,通过对模拟数据的可视化和交互式探索可以极大提高科学家的分析效率.随着超级计算机的发展和宇宙学理论的不断完善,数值模拟的规模越来越大并且精度越来越高,这对可视化产生了多种分析需求和数据处理挑战.本文概述了宇宙数值模拟中可视化的主要方法,并且通过多个研究案例展示了可视化方法在宇宙模拟数据分析中的作用.最后,本文总结了当前研究的热点和面临的挑战.     

基于VaR技术的多维数据交互可视化研究

传统多维数据可视化技术比如平行坐标法等在表达多维数据时,对各个不同数据维之间的数据表达式会产生一定的混乱,同时对各维的相关性与交互表达比较困难。研究并设计了基于VaR技术的多维数据交互可视化小工具,其充分利用VaR技术所实现的丰富导航与选择、突出等工具来形象突出显示与表达多维数据中某一特定维的数据,同时应用MDS算法,在二维屏幕上各点的相对位置关系来表示多维数据中各维的相关性。     

一个基于中心差分技术的实时高度场可视化方法

本文提出了一种基于中心差分技术进行高度场镶嵌的系统方法,结合视点相关技术实现了多分辨率模型的生成,以适应实时应用的需要。此外,结合数据的预处理使本文的方法也能适用于真实采样数据的图形生成。本文的方法可用来实现对数据高度场进行实时图形显示的目的。与同类方法相比,本方法灵活性高,能够适于多种应用,其视觉质量好并且执行效率高。     

基于VaR技术的多维数据交互可视化研究

传统多维数据可视化技术比如平行坐标法等在表达多维数据时,对各个不同数据维之间的数据表达式会产生一定的混乱,同时对各维的相关性与交互表达比较困难。研究并设计了基于VaR技术的多维数据交互可视化小工具,其充分利用VaR技术所实现的丰富导航与选择、突出等工具来形象突出显示与表达多维数据中某一特定维的数据,同时应用MDS算法,在二维屏幕上各点的相对位置关系来表示多维数据中各维的相关性。     

多用户眼动跟踪数据的可视化共享与协同交互

随着数字图像处理技术的发展,以及计算机支持的协同工作研究的深入,眼动跟踪开始应用于多用户协同交互.但是已有的眼动跟踪技术主要针对单个用户,多用户眼动跟踪计算架构不成熟、标定过程复杂,眼动跟踪数据的记录、传输以及可视化共享机制都有待深入研究.为此,建立了基于梯度优化的协同标定模型,简化多用户的眼动跟踪标定过程;然后提出面向多用户的眼动跟踪计算架构,优化眼动跟踪数据的传输和管理.进一步地,探索眼动跟踪数据的可视化形式在协同交互环境下对用户视觉注意行为的影响,具体设计了圆点、散点、轨迹这3种可视化形式,并验证了圆点形式能够有效地提高多用户协同搜索任务的完成效率.在此基础上,设计与开发了基于眼动跟踪的代码协同审查系统,实现了代码审查过程中多用户眼动跟踪数据的同步记录、分发,以及基于实时注视点、代码行边框和背景灰度、代码行之间连线的可视化共享.用户实验结果表明,代码错误的平均搜索时间比没有眼动跟踪数据可视化分享时减少了20.1%,显著提高了协同工作效率,验证了该方法的有效性.     

面向科学计算可视化的两级并行数据读取加速方法

为了匹配超级计算机的整体计算能力,超级计算机存储子系统通常具有良好的I/O性能可扩展性,表现为：应用获得存储子系统最佳性能时的I/O访问并发度,与超级计算机系统总计算核数(可达数万至数百万)通常处于同一数量级.然而,科学计算可视化应用通常使用的进程数(等于I/O访问并发度)相对较小(经验上常设为计算进程数的1%,典型值为数个至数百个),因此无法充分发挥超级计算机存储子系统的最佳I/O性能.提出了一种面向科学计算可视化的两级并行数据读取加速方法,在可视化进程内部引入多线程并行数据读取,通过进程间和进程内两级并行,增加超级计算机存储子系统的I/O访问并发度,提升可视化应用数据读取速率.测试结果表明:在不同的可视化进程规模下,两级并行比单级并行峰值数据读取速率提高33.5%~269.5%,均值数据读取速率提高26.6%~232.2%;随着科学计算应用种类以及应用规模的变化,两级并行数据读取可使可视化应用整体峰值运行速度加速19.5%~225.7%,均值运行速度加速15.8%~197.6%.