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 Opportunitiesâ€, 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.
