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

随着科学家们对艾级计算的好处的预期，缺乏大规模处理数据和校准模拟参数的新解决方案已成为进一步加速科学发现的重大障碍。该项目的目标是基于深度神经网络开发一种新的端到端数据分析和特征提取工作流程，以帮助计算科学家解决三大挑战：（1）识别重要的仿真参数并生成用于分析的基本数据，（2）将仿真数据转换为紧凑的特征表示以传达最深入的见解，以及（3）设计可扩展的可视化算法，并结合大规模模拟，以深入了解他们的科学问题。该团队将与喷气发动机设计、气候模型、心脑血管流量、超导性和聚变能领域的科学家合作，展示深度学习技术如何帮助从大量模拟数据中提取特征，并在巨大的模拟参数空间中导航。通过暑期实习和项目合作，这项研究将为研究生和本科生创造机会，包括来自代表性不足群体的学生，与领先的科学家一起参与关键的研究计划。通过计划中的年度暑期学校“深度学习可视化”，研究成果将使可视化研究人员和更广泛的社区能够结合开发的深度学习技术的原理和实践。该研究团队将开发一个全面的分析框架，其中包括一套最先进的深度学习技术，用于大规模科学模拟数据的原位处理和分析。该框架将由三个紧密集成的组件组成：（1）模拟参数和数据简化的分析，（2）数据和功能的后分析，以及（3）现场工作流程优化。对于第一个组成部分，方法将被开发，以协助模拟代理创建，参数空间探索，并比较分析合奏模拟。对于第二个组成部分，将开发深度学习技术，以从数据中学习特征，用于代表的交互式探索，并在空间和时间域中放大减少的模拟输出。对于第三部分，将为特征检测、工作量估计和特征计算代理开发现场解决方案。该框架将使用四种类型的定量指标进行评估：数据缩减率，数据，特征和图像级错误度量，可扩展性度量以及与训练和测试数据的交叉验证。该团队将与喷气发动机，气候，心/脑血管流量，超导和聚变能领域的科学家密切合作。领域科学家将发挥关键作用，使研究团队能够了解其应用程序的要求，并评估这项研究的成果。该项目的传播计划将面向更广泛的受众，包括学生、从业者和领域科学家，以增强他们对深度学习可视化价值的理解和欣赏。该团队将发布开源软件，预训练模型以及从这项研究中生成的训练和测试数据，包括用于特征学习的自动编码器，DNN辅助参数空间探索，基于CNN的特征提取和跟踪，以及基于负载平衡的深度预测模型。该奖项包括来自信息智能系统部门的信息集成&信息学计划&，&计算机计算基础部门的软件硬件系统计划&，该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.