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

HDR：I-DIRSE-FW：利用数据科学加速工程设计和制造生命周期

• 批准号: 1934292
• 负责人: Magdalena Balazinska
• 金额: $ 200万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1934292&HistoricalAwards=false
• 关键词: HDR; DIRSE; FW; Accelerating; Engineering

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.

制造生命周期始于新分子和材料的发现。第一步通常是通过计算机模拟来启动，计算机模拟探索可能的分子和材料的空间，并确定有前途的候选物，以便稍后在实验室进行测试。随着模拟规模和复杂性的增长，这一步已成为关键瓶颈。新的数据驱动方法提供了提高此类预测速度和准确性的机会，对美国制造业具有广泛的潜在影响。利用数据革命研究所进行科学与工程数据密集型研究 (HDR-I-DIRSE) 框架奖将工程师和数据科学家聚集在一起，构思一个新的工程数据科学研究所，在该研究所中，这些工具可用于新的发现。这项工作将开发新的数据科学方法来加速工程生命周期：设计、表征、制​​造和运营。这个生命周期从新分子和材料的发现开始，然后通过机器学习增强的高通量方法进行高级表征。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。通过关注这个整体生命周期，研究人员将为工程数据科学方法奠定广泛适用的基础。新研究所将寻求创建一个工程数据科学环境，通过一系列协同的协作和教育活动为工程师和科学家（学生、博士后研究人员和教师）提供支持。这种协作努力遵循三个主旨。第一个重点是利用旨在发现新分子和聚合物的数据科学工具来减少实验设计空间。该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模型配对，以创建可扩展且可转移的工作流程，可以跨分子工程应用的多个示例进行部署。第二个重点解决了图像数据分析与材料和系统表征交叉点的多种交叉需求。它还通过开源软件资源构建社区网络基础设施，并支持在公共云中执行。最后的重点是改进制造、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，系统地开发复杂工程和制造系统的数字孪生模型，并将其应用于优化和控制。该项目是美国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评估进行评估，认为值得支持。优点和更广泛的影响审查标准。

项目成果

Effective Laboratory Education with TEXTILE: Tutorials in EXperimentalisT Interactive LEarning

纺织品的有效实验室教育：实验互动学习教程

• DOI: 10.18260/2-1-370.660-129820
• 发表时间: 2022-01
• 期刊: Chemical Engineering Education
• 作者: Helmbrecht; Hawley
• 通讯作者: Hawley

Organotypic whole hemisphere brain slice models to study the effects of donor age and oxygen-glucose-deprivation on the extracellular properties of cortical and striatal tissue

器官型全半球脑切片模型，用于研究供体年龄和缺氧葡萄糖对皮质和纹状体组织细胞外特性的影响

• DOI: 10.1186/s13036-022-00293-w
• 发表时间: 2022-06-13
• 期刊: Journal of Biological Engineering
• 影响因子: 5.6
• 作者: McKenna, Michael;Filteau, Jeremy R.;Butler, Brendan;Sluis, Kenneth;Chungyoun, Michael;Schimek, Nels;Nance, Elizabeth
• 通讯作者: Nance, Elizabeth

A hybrid open-top light-sheet microscope for versatile multi-scale imaging of cleared tissues

混合式开顶光片显微镜，可对透明组织进行多功能多尺度成像

• DOI: 10.1038/s41592-022-01468-5
• 发表时间: 2022-05-01
• 期刊: Nature Methods
• 影响因子: 48
• 作者: A. Glaser;K. Bishop;L. Barner;E. Susaki;Shimpei I. Kubota;Gan Gao;R. Serafin;P. Balaram;Emily Turschak;P. Nicovich;H. Lai;L. Lucas;Yating Yi;Eva K. Nichols;Hongyi Huang;N. Reder;Jasmine Wilson;R. Sivakumar;E. Shamskhou;C. Stoltzfus;Xing Wei;Andrew K. Hempton;M. Pende;P. Murawala;H. Dodt;T. Imaizumi;J. Shendure;Brian J. Beliveau;Michael Y. Gerner;Li Xin;Hu Zhao;L. True;R. Reid;J. Ch;rashekar;rashekar;H. Ueda;K. Svoboda;Jonathan T. C. Liu
• 通讯作者: Jonathan T. C. Liu

Engineering the future of 3D pathology

设计 3D 病理学的未来

• DOI: 10.1002/cjp2.347
• 发表时间: 2023-11
• 期刊: The Journal of Pathology: Clinical Research
• 作者: Liu, Jonathan TC;Chow, Sarah SL;Colling, Richard;Downes, Michelle R.;Farré, Xavier;Humphrey, Peter;Janowczyk, Andrew;Mirtti, Tuomas;Verrill, Clare;Zlobec, Inti;et al
• 通讯作者: et al

Brain Tissue-Derived Extracellular Vesicle Mediated Therapy in the Neonatal Ischemic Brain

脑组织源性细胞外囊泡介导的新生儿缺血性脑治疗

• DOI: 10.3390/ijms23020620
• 发表时间: 2022-01-06
• 期刊: International Journal of Molecular Sciences
• 影响因子: 5.6
• 作者: Nguyen NP;Helmbrecht H;Ye Z;Adebayo T;Hashi N;Doan MA;Nance E
• 通讯作者: Nance E