III: Medium: Collaborative Research: Deep Learning for In Situ Analysis and Visualization

III：媒介：协作研究：用于原位分析和可视化的深度学习

• 批准号: 1955395
• 负责人: Chaoli Wang
• 金额: $ 48.03万
• 依托单位: University of Notre Dame
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1955395&HistoricalAwards=false
• 关键词: III; Medium; Collaborative; Research; Deep

As scientists anticipate the benefits of exascale computing, the lack of novel solutions to process data at scale and calibrate the simulation parameters has become a significant roadblock to further accelerating scientific discovery. The goal of this project is to develop a new end-to-end data analysis and feature extraction workflow based on deep neural networks to help computational scientists address three major challenges: (1) identify important simulation parameters and generate the essential data for analysis, (2) transform the simulation data to compact feature representations to convey the most insight, and (3) design scalable visualization algorithms coupled with large-scale simulations to glean insight into their scientific problems. Working with domain scientists in jet engine design, climate models, cardio/cerebrovascular flow, superconductivity, and fusion energy, the team will demonstrate how deep learning techniques can help extract features from vast amounts of simulation data and navigate in the huge simulation parameter space. Through summer internships and project collaborations, this research will create opportunities for graduate and undergraduate students, including students from underrepresented groups, to participate in key research initiatives with leading scientists. Through the planned annual summer school on "Deep Learning for Visualization," the research results will enable visualization researchers and a broader community to incorporate the principles and practice of deep learning techniques developed. The research team will develop a comprehensive analysis framework that encompasses a suite of state-of-the-art deep learning techniques for in situ processing and analysis of large-scale scientific simulation data. The framework will consist of three tightly-integrated components: (1) analysis of simulation parameters and data reduction, (2) post-analysis of data and features, and (3) in situ workflow optimization. For the first component, methods will be developed to assist simulation surrogate creation, parameter space exploration, and comparative analytics of ensemble simulations. For the second component, deep learning techniques will be developed to learn features from data for interactive exploration of representatives and to upscale reduced simulation output in the spatial and temporal domains. For the third component, in situ solutions will be developed for feature detection, workload estimation, and feature computation surrogates. The framework will be evaluated using four types of quantitative metrics: data reduction ratio, data-, feature-, and image-level error measures, scalability measures, and cross-validation with training and testing data. The team will work closely with scientists in the domains of jet engine, climate, cardio/cerebrovascular flow, superconductivity, and fusion energy. The domain scientists will play a critical role in enabling the research team to understand the requirements of their applications and to evaluate the outcomes of this research. The project's dissemination plan will address a much broader audience, including students, practitioners, and domain scientists, to enhance their understanding and appreciation of the value of deep learning for visualization. The team will release open-source software, pre-trained models, and training and test data generated from this research, including auto-encoder for feature learning, DNN-assisted parameter space exploration, CNN-based feature extraction and tracking, and load-balancing based deep predictive models.This award includes funding from the Information Integration & Informatics Program in the Division of Information & Intelligent Systems, Software & Hardware Systems Program in the Division of Computer & Computing Foundations, and the NSF Office of Advanced Cyberinfrastructure.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

正如科学家预期的是Exascale计算的好处一样，缺乏大规模处理数据并校准模拟参数的新颖解决方案已成为进一步加速科学发现的重要障碍。 The goal of this project is to develop a new end-to-end data analysis and feature extraction workflow based on deep neural networks to help computational scientists address three major challenges: (1) identify important simulation parameters and generate the essential data for analysis, (2) transform the simulation data to compact feature representations to convey the most insight, and (3) design scalable visualization algorithms coupled with large-scale simulations to glean insight into their scientific problems.该团队与喷气发动机设计，气候模型，心脏/脑血管流，超导性和融合能量的领域科学家合作，该团队将展示深度学习技术如何帮助从大量仿真数据中提取功能并在巨大的仿真参数空间中导航。通过暑期实习和项目合作，这项研究将为包括代表性不足小组的学生在内的研究生和本科生创造机会，以与主要科学家一起参加重要的研究计划。通过计划的年度暑期学校“深度学习可视化”，研究结果将使可视化研究人员和更广泛的社区能够纳入开发的深度学习技术的原理和实践。研究团队将开发一个全面的分析框架，该框架涵盖了一套最先进的深度学习技术，用于原位处理和大规模科学模拟数据的分析。该框架将由三个紧密集成的组件组成：（1）模拟参数和数据降低的分析，（2）数据和特征的分析后，以及（3）原位工作流程优化。对于第一个组件，将开发方法来协助模拟替代物创建，参数空间探索和集合模拟的比较分析。对于第二个组件，将开发深度学习技术，以从数据中学习特征，以探索代表的交互式探索以及在空间和时间域中的高档模拟输出。对于第三个组件，将开发原位解决方案以进行特征检测，工作负载估计和特征计算替代物。将使用四种类型的定量指标评估该框架：数据降低比率，数据，功能 - 和图像级误差度量，可伸缩性度量以及通过培训和测试数据进行交叉验证。该团队将与喷气发动机，气候，心脏/脑血管流，超导性和融合能的科学家紧密合作。领域科学家将在使研究团队了解其应用程序的要求并评估这项研究的结果方面发挥关键作用。该项目的传播计划将对包括学生，从业人员和领域科学家在内的更广泛的受众群体介绍，以增强他们对可视化深度学习价值的理解和欣赏。 The team will release open-source software, pre-trained models, and training and test data generated from this research, including auto-encoder for feature learning, DNN-assisted parameter space exploration, CNN-based feature extraction and tracking, and load-balancing based deep predictive models.This award includes funding from the Information Integration & Informatics Program in the Division of Information & Intelligent Systems, Software & Hardware Systems Program in the Division of Computer & Computing Foundations, and the NSF高级网络基础设施办公室。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估获得支持。

项目成果

Reconstructing Unsteady Flow Data From Representative Streamlines via Diffusion and Deep-Learning-Based Denoising

• DOI: 10.1109/mcg.2021.3089627
• 发表时间: 2021-11-01
• 期刊: IEEE COMPUTER GRAPHICS AND APPLICATIONS
• 影响因子: 1.8
• 作者: Gu, Pengfei;Han, Jun;Wang, Chaoli
• 通讯作者: Wang, Chaoli

VCNet: A generative model for volume completion

• DOI: 10.1016/j.visinf.2022.04.004
• 发表时间: 2022-04
• 期刊: Vis. Informatics
• 作者: Jun Han;Chaoli Wang

NeRVI: Compressive neural representation of visualization images for communicating volume visualization results

• DOI: 10.1016/j.cag.2023.08.024
• 发表时间: 2023-08
• 期刊: Comput. Graph.
• 作者: Pengfei Gu;Da Chen;Chaoli Wang

Designing a Learning Analytics Dashboard to Provide Students with Actionable Feedback and Evaluating Its Impacts

设计学习分析仪表板，为学生提供可行的反馈并评估其影响

• DOI: 10.5220/0011116400003182
• 发表时间: 2022
• 期刊: Proceedings of International Conference on Computer Supported Education
• 作者: Duan, Xiaojing;Wang, Chaoli;Rouamba, Guieswende
• 通讯作者: Rouamba, Guieswende

Towards transparent and trustworthy prediction of student learning achievement by including instructors as co-designers: a case study

通过将教师作为共同设计者来实现对学生学习成绩的透明且值得信赖的预测：案例研究

• DOI: 10.1007/s10639-023-11954-8
• 发表时间: 2023
• 期刊: Education and Information Technologies
• 影响因子: 5.5
• 作者: Duan, Xiaojing;Pei, Bo;Ambrose, G. Alex;Hershkovitz, Arnon;Cheng, Ying;Wang, Chaoli
• 通讯作者: Wang, Chaoli