THREE SHORT COURSES ARE PLANNED FOR THE FIRST IACM HYBRID CONFERENCE ON
MECHANISTIC MACHINE LEARNING AND DIGITAL TWIN FOR COMPUTATIONAL SCIENCE, ENGINEERING AND TECHNOLOGY
(MMLDT-CSET) TO BE HELD IN SAN DIEGO, SEPTEMBER 26-29, 2021 (https://mmldt.eng.ucsd.edu/)
Data science methodologies require copious quantities of data to show a reliable pattern which can be used to draw conclusions. The amount of data required to find a solution can be greatly reduced by considering the mathematical science principles. Mechanistic data science (MDS) combines mathematical scientific principles with available data.
Mathematical scientific principles provide the fundamental understanding of the world and allow predictions which drive new discoveries and enable future technologies. Unfortunately, development of new scientific principles is often trailing the pace of new inventions. The ability to combine known scientific principles with newly collected data will be a boon for new inventions.
Three short courses are being planned to promote awareness of MDS STEM Education and Applications for STEM high school students, STEM high school teachers, STEM undergraduates, graduates and practicing engineers and scientists.
• Mechanistic Data Science for STEM Education and Applications is specially designed for STEM high school students. It will provide them with a broad perspective for coupling data science tools with basic high school mathematics and elementary scientific principles and customized mini-apps tools to solve fact-finding scientific and engineering problems. Daily-life examples will be used to demonstrate key concepts.
• Mechanistic Data Science for Engineering and Science and Applications is designed for high school teachers and STEM undergraduates. It will provide them with a broad perspective for coupling data science tools with continuous and discrete mathematics and scientific principles and customized mini-apps tools to solve intractable problems. It will also introduce them the basic machine learning tools for hands-on problem solving experience.
• Mechanistic Machine Learning for Physics and Mechanical Science will be offered to graduate students and researchers to introduce them to practical data analytics, data reduction and machine learning techniques, to be applied in a variety of science and engineering applications (e.g., materials, processes, structures, systems, etc.) and a variety of digital twins.
To enhance and appreciate the learning experience of these courses, sessions under a three-day Technical Track 8 “Education, Outreach, and Funding Opportunities” will be developed for the mentoring and networking activities, and panels and Q&A. Panel discussions on funding opportunities, mechanistic data science education and outreach will also be organized to brainstorm ideas and provide recommendations on knowledge dissemination and the promotion of research and education in machine learning and digital twins to the communities. Panel members from academia, industry, government agencies, and students will be invited to participate in the panel discussions.