HDR: I-DIRSE-FW: Accelerating the Engineering Design and Manufacturing Life-Cycle with Data Science

制造生命周期始于新分子和新材料的发现。这第一步通常是通过计算机模拟来启动的，这些计算机模拟探索可能的分子和材料的空间，并确定有希望的候选者，稍后可以在实验室进行测试。随着模拟规模和复杂性的增长，这一步已经成为一个关键的瓶颈。新的数据驱动方法为提高此类预测的速度和准确性提供了机会，对美国制造业具有广泛的潜在影响。这项利用数据革命科学与工程数据密集型研究研究所(HDR-I-DIRSE)框架奖的活动将工程师和数据科学家聚集在一起，构思出一个新的工程数据科学研究所，在那里可以应用这些工具进行新的发现。这项工作将开发新的数据科学方法，以加快工程生命周期：设计、表征、制造和运营。这一生命周期始于新分子和新材料的发现，随后是机器学习增强的高通量方法的高级表征。然后，可以设计和开发使用这些材料的系统的高效制造和操作。通过关注这一整体生命周期，研究人员将在工程数据科学方法方面建立一个广泛适用的基础。新的研究所将寻求通过一系列协作和教育活动来创建一个支持工程师和科学家(学生、博士后研究人员和教职员工)的工程数据科学环境。第一个重点是利用数据科学工具缩小实验设计空间，目标是发现新的分子和聚合物。这项研究开发了一个新的正式框架，用于将准确的预测模拟与数据驱动的模型配对，以创建可扩展和可转移的工作流程，该工作流程可以部署在分子工程应用的多个示例中。第二个推力涉及图像数据分析和材料和系统表征的交叉点上的多种交叉需求。它还通过支持在公共云中执行的开源软件资源来构建社区网络基础设施。最后一项重点是改进制造、优化和控制。它通过一套开源软件解决方案进一步增强网络基础设施资源，系统地为复杂的工程和制造系统开发数字孪生模型，并将其应用于优化和控制。该项目是国家科学基金会利用数据革命(HDR)大创意活动的一部分，由高级网络基础设施办公室共同资助。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

The manufacturing life cycle begins with the discovery of new molecules and materials. This first step is often initiated through computer simulations that explore the space of possible molecules and materials, and identify promising candidates that can later be tested in laboratories. As simulations have grown in scale and complexity, this step has become a critical bottleneck. New data-driven approaches present the opportunity to increase the speed and accuracy of such predictions, with broad potential impact on the US Manufacturing sector. This Harnessing the Data Revolution Institutes for Data-Intensive Research in Science and Engineering (HDR-I-DIRSE) Frameworks award brings together Engineers and Data Scientists to conceptualize a new Engineering Data Science Institute where these tools can be applied for new discovery. The effort will develop new data science approaches to accelerate the engineering life cycle: design, characterization, manufacturing, and operation. This life cycle starts with the discovery of new molecules and materials, followed by advanced characterization with high throughput methods augmented by machine learning. Then, efficient manufacturing and operation of systems that use these materials can be designed and developed. By focusing on this holistic lifecycle, the researchers will build a broadly applicable foundation in Engineering Data Science methods. The new Institute will seek to create an Engineering Data Science environment that supports engineers and scientists (students, postdoctoral researchers, and faculty) through a synergistic set of collaboration and education activities.This collaborative effort follows three thrusts. The first focuses on the reduction of the experimental design space with data science tools targeting the discovery of new molecules and polymers. The research develops a new, formal framework for pairing accurate predictive simulations with data-driven models to create a scalable and transferable workflow that can be deployed across multiple examples of molecular engineering applications. The second thrust addresses a manifold of cross-cutting needs at the intersection of image data analytics and characterization of materials and systems. It also builds community cyberinfrastructure through open-source software resources with support for execution in public clouds. The final thrust focuses on improving manufacturing, optimization, and control. It further enhances cyberinfrastructure resources through a suite of open-source software solutions to systematically develop digital twin models for complex engineering and manufacturing systems, and apply them for optimization and control. This project is part of the National Science Foundation's Harnessing the Data Revolution (HDR) Big Idea activity and is co-funded by the 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.