NRT-AI: Harnessing AI for Inverse Design Training in Advanced and Sustainable Composites (IDeAS Composites)

NRT-AI：利用人工智能进行先进和可持续复合材料的逆向设计培训（IDeAS Composites）

• 批准号: 2244342
• 负责人: Gang Li
• 金额: $ 300万
• 依托单位: Clemson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-15 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2244342&HistoricalAwards=false
• 关键词: NRT; AI; Harnessing; Inverse; Design

Despite the vast design space of composites, there are significant gaps between the performance, economic, and environmental targets and current design and manufacturing approaches. Most egregious are the expensive, long development cycles and the sub-optimal design that waste resources and may adversely affect the environment and climate change. The fundamental cause of such gaps is the lack of detailed understanding of the influence of the material architecture, process methods, and parameters on material microstructure evolution and subsequently the end product’s physical, economic, and environmental performance. This National Science Foundation Research Traineeship (NRT), harnessing artificial intelligence (AI) for Inverse Design Training in Advanced and Sustainable Composites (IDeAS), will train students through a physics-informed, AI-based modeling and design platform which will enable the discovery of new composites materials forms and relevant new manufacturing methodologies. This NRT award to Clemson University will catalyze a shift in the research and discovery pathway of the trainees via a transformative AI-age curriculum co-instructed by academic faculty and industry researchers. The IDeAS Composites program will train a total of 50 students; of these, 25 will be NRT-funded IDeAS fellows and the remaining 25 would be identified as IDeAS scholars. The program will draw trainees from computer science, data science, statistical science, mechanical engineering, automotive engineering, and materials science and will empower trainees with an academia–industry co-trained skill set that will ensure their success in the AI age. The research theme of this NRT program is focused on discovering and investigating the effectiveness of a physics-informed, machine-learning-based inverse design platform for developing new composite material architectures and manufacturing methodologies. The program will train a cohort of graduate students with deep, specialized expertise supported by broad, cross-skill knowledge, and equip them with a unique “DNA-shaped” skill set collaboratively facilitated by both academic and industry experts. Specifically, the program will (1) catalyze interdisciplinary research at the intersection of AI and the inverse design of composites and manufacturing innovation via constructing a “digital life cycle” which is a suite of high-fidelity models for simulating a composite component’s life cycle, investigating the application of machine-learning methods for inverse composite material architecture and manufacturing process design, and developing an inverse design approach for integrated material and manufacturing design; (2) explore a combined graduate and undergraduate student training model comprising a composites inverse design capstone and a research design, development, and demonstration (RD&D) project centering on research outcomes applied to industry problems; (3) create a diverse, equitable, and inclusive environment fostering interdisciplinary collaboration in which trainees are prepared for careers requiring a unique “DNA-shaped” skill set; and (4) establish an interdisciplinary education program to (a) prepare next-generation composites engineering graduates (altogether 50 by year 5) who will have AI-enabled inverse design expertise and skills necessary to meet the unique challenges of the coming AI age and to thrive in the composites industry, and (b) train the current workforce to enhance their knowledge and foster dissemination of AI methods and principles in the composites engineering community.The NSF Research Traineeship (NRT) Program is designed to encourage the development and implementation of bold, new potentially transformative models for STEM graduate education training. The program is dedicated to effective training of STEM graduate students in high priority interdisciplinary or convergent research areas through comprehensive traineeship models that are innovative, evidence-based, and aligned with changing workforce and research needs.  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.

尽管复合材料的设计空间巨大，但性能，经济和环境目标与当前的设计和制造方法之间存在显着差距。最优雅的是昂贵的，长长的开发周期和浪费资源的次优设计，并可能对环境和气候变化产生不利影响。这种差距的基本原因是对材料架构，过程方法和参数对材料微观结构演变的影响以及随后最终产品的物理，经济和环境性能的影响缺乏详细的了解。这项国家科学基金会研究训练（NRT），利用人工智能（AI）进行高级和可持续复合材料的逆设计培训（思想），将通过物理知识，基于AI的建模和设计平台来培训学生，该平台将能够发现新的复合材料材料材料形式和相关的新制造方法。授予克莱姆森大学的NRT奖将通过学术教职员工和行业研究人员共同实现学术的AI-AGE课程来促进受训者的研究和发现途径的转变。创意复合材料计划将培训总计50名学生；其中，有25个将是NRT资助的想法研究员，其余25位将被确定为学者。该计划将吸引计算机科学，数据科学，统计科学，机械工程，汽车工程和材料科学的受训者，并将通过学术界 - 工业共同培训的技能促进学员的能力，以确保他们在AI时代的成功。该NRT计划的研究主题侧重于发现和调查物理知识，基于机器学习的逆设计平台，以开发新的复合材料体系结构和制造方法。计划将培训一群具有深厚专业知识的研究生，并以广泛的跨技能知识为支持，并为他们配备了由学术和行业专家共同准备的独特“ DNA形”技能。具体而言，该计划将（1）通过构建“数字生命周期”来催化跨学科研究与复合材料和制造创新的逆设计和制造创新，这是一套高保真模型的套件，用于模拟复合组件的生命周期，用于在机器实质架构和制造材料设计中的应用，并整合化合物设计和制造工艺设计，并开发了一项制造方法，并开发了一套工艺设计，并开发了一系列的设计，并开发了一套化合物的设计，并开发了一套化合物的设计，并开发了一套化合物。 （2）探索一个合并的研究生和本科生培训模型，该模型包括组成逆设计顶点以及研究设计，开发和示范（RD＆D）项目，该项目以针对行业问题的研究成果为中心； （3）创建一个潜水员，公平且包容性的环境，以促进跨学科合作，在该协作中，学员为需要独特的“ DNA形”技能的职业做好准备； and (4) establish an interdisciplinary education program to (a) prepare next-generation compositions engineering graduates (altogether 50 by year 5) who will have AI-enabled inverse design expertise and skills necessary to meet the unique challenges of the coming AI age and to thrive in the Composites industry, and (b) train the current workforce to enhance their knowledge and foster dissemination of AI methods and principles in the composition engineering community.The NSF研究实习生（NRT）计划旨在鼓励开发和实施用于STEM研究生教育培训的大胆，新的潜在变革模型。该计划致力于通过全面的跨学科或收敛性研究领域的STEM研究生进行有效培训，通过全面的培训模型，这些模型具有创新，基于循证的，并且与不断变化的劳动力和研究需求保持一致。该奖项反映了NSF的法定使命，并通过使用基金会的知识优点和广泛的影响来评估NSF的法定任务，并被认为是宝贵的支持。