Mechanistic Machine Learning and Digital Twins for Computational Science, Engineering and Technology (MMLDT-CSET) Conference 2021; San Diego, California; September 26-29, 2021

这次为期3天的会议由14个技术轨道和两个混合形式的短期课程组成。这次会议将提供一个论坛，让学生、研究人员、高中教师和实践者在机械机器学习、人工智能和数字双胞胎领域交流新思想。应用领域包括民用基础设施、自然灾害工程、地球系统和石油工程、基于可靠性的工程和设计、材料系统、制造、数学和科学计算、自然科学和生命科学以及医疗保健。面向高中生和教师以及STEM本科生的一门名为《机械数据科学》(MDS)的短期课程旨在为学员提供与机器学习和数字双胞胎相关的宏观视角，并通过日常生活中的例子演示如何应用MDS将数据科学工具与数学科学原理相结合来解决棘手的问题。将为研究生和研究人员提供一个名为《物理和力学的机械机器学习》的短期课程，向具有物理和力学背景的参与者介绍机器学习技术。这些课程还将与技术轨道“教育、外联和筹资机会”下的指导和联网活动相结合，并与小组讨论和三天中每一天的问答活动相结合。NSF奖学金将用于支持本科生和研究生、博士后研究员、高中教师和学生以及贫困学校的学生参加会议和短期课程活动。来自历史上黑人学院和大学、少数族裔服务机构以及贫困高中的本科生和研究生将被招募。这次会议介绍了作为一种将数据与数学和科学原理结合起来解决其他棘手问题的综合方法论的机器学习和数字双胞胎(MMLDT)。本次会议还将“数字双胞胎”确定为通过计算科学、工程和技术(CSET)改进产品设计的重要机器学习应用程序。MMLDT-CSET的主要目标是将这些对学习、开发和应用机器学习和数字双胞胎感兴趣的社区聚集在一起，通过机械方法和计算科学和工程工具解决广泛的工程和科学问题，同时促进来自联邦机构、学术界和工业界的工程师、物理和生物科学家、数据和计算机科学家以及数学家之间的跨学科合作。对未来MMLDT研究和技术发展的讨论将受到社会需求和执业工程师、技术公司和计算机/软件公司提出的重大挑战的推动。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。

This 3-day conference is organized into fourteen technical tracks and two short courses in a hybrid format. This conference will provide a forum to exchange new ideas among students, researchers, high school teachers, and practitioners in the fields of mechanistic machine learning, artificial intelligence, and digital twins. Application areas include civil infrastructures, natural hazards engineering, geosystems and petroleum engineering, reliability-based engineering and design, material systems, manufacturing, mathematical and scientific computing, natural and life sciences, and healthcare. A short course called “Mechanistic Data Science” (MDS) targeted at high school students and teachers, and STEM undergraduates is designed to provide participants with a big-picture perspective related to machine learning and digital twins, and to demonstrate how to apply MDS to combine data science tools with mathematical scientific principles to solve intractable problems through daily-life examples. A short course called “Mechanistic Machine Learning for Physics and Mechanics” will be offered for graduate students and researchers to introduce machine learning techniques for the participants with a background in physics and mechanics. These courses will also be integrated with the mentoring and networking activities under a Technical Track “Education, Outreach, and Funding Opportunities”, and with a panel and Q&A sessions for each of the three days. NSF Fellowship will be used to support undergraduate and graduate students, postdoctoral fellows, high school teachers and students, as well as students from underprivileged schools to attend the conference and short course activities. Undergraduate and graduate students from historically black colleges and universities and minority-serving institutions as well as underprivileged high schools will be recruited.This conference introduces “Mechanistic” Machine Learning and Digital Twins (MMLDT) as an integrated methodology for coupling data with mathematics and scientific principles to solve otherwise intractable problems. This conference also identifies “Digital Twins” as important machine learning applications for improved product designs via computational science, engineering, and technology (CSET). The main objective of MMLDT-CSET is to bring together these diverse communities that are interested in learning, developing, and applying machine learning and digital twins via mechanistic methods and computational science and engineering tools for a broad range of engineering and scientific problems, while promoting transdisciplinary collaborations among engineers, physical and biological scientists, data and computer scientists, and mathematicians from federal agencies, academia, and industry. The discussion of future MMLDT research and technology developments will be driven by societal needs and grand challenges presented by practicing engineers, technology firms, and computer/software companies.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.