{"id":206875,"date":"2025-09-07T05:42:16","date_gmt":"2025-09-07T05:42:16","guid":{"rendered":"https:\/\/www.europesays.com\/us\/206875\/"},"modified":"2025-09-07T05:42:16","modified_gmt":"2025-09-07T05:42:16","slug":"ai-and-machine-learning-for-engineering-design-mit-news","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/us\/206875\/","title":{"rendered":"AI and machine learning for engineering design | MIT News"},"content":{"rendered":"

Artificial intelligence optimization offers a host of benefits for mechanical engineers, including faster and more accurate designs and simulations, improved efficiency, reduced development costs through process automation, and enhanced predictive maintenance and quality control.<\/p>\n

\u201cWhen people think about mechanical\u00a0engineering, they’re thinking about basic mechanical tools like\u00a0hammers and \u2026 hardware like cars, robots, cranes, but mechanical engineering is very broad,\u201d says Faez Ahmed, the Doherty Chair in Ocean Utilization and associate professor of mechanical engineering at MIT. \u201cWithin mechanical engineering,\u00a0machine learning, AI, and optimization are playing a big role.\u201d<\/p>\n

In Ahmed\u2019s course, 2.155\/156<\/a> (AI and Machine Learning for Engineering Design), students use tools and techniques from artificial intelligence and machine learning for mechanical engineering design, focusing on the creation of new products and addressing engineering design challenges.<\/p>\n

<\/p>\n

\"Video<\/p>\n

Play video<\/a><\/p>\n

Cat Trees to Motion Capture: AI and ML for Engineering Design

\nVideo: MIT Department of Mechanical Engineering <\/p>\n

\u201cThere\u2019s a lot of reason for mechanical engineers to think about machine learning and AI to essentially expedite the design process,\u201d says Lyle Regenwetter, a teaching assistant for the course and a PhD candidate in Ahmed\u2019s Design Computation and Digital Engineering Lab (DeCoDE), where research focuses on developing new machine learning and optimization methods to study complex engineering design problems.<\/p>\n

First offered in 2021, the class has quickly become one of the Department of Mechanical Engineering (MechE)\u2019s most popular non-core offerings, attracting students from departments across the Institute, including mechanical and civil and environmental engineering, aeronautics and astronautics, the MIT Sloan School of Management, and nuclear and computer science, along with cross-registered students from Harvard University and other schools.<\/p>\n

The course, which is open to both undergraduate and graduate students, focuses on the implementation of advanced machine learning and optimization strategies in the context of real-world mechanical design problems.\u00a0From designing\u00a0bike frames to city grids, students participate in contests related to AI for physical systems and\u00a0tackle optimization challenges in a class environment fueled by friendly competition.<\/p>\n

Students are given challenge problems and starter code that \u201cgave a solution, but [not] the best solution \u2026\u201d explains Ilan Moyer, a graduate student in MechE. \u201cOur task was to [determine],\u00a0how can we do better?\u201d Live leaderboards encourage students to continually refine their methods.<\/p>\n

Em Lauber, a system design and management graduate student, says the process gave space to explore the application of what students were learning and the practice skill of \u201cliterally how to code it.\u201d<\/p>\n

The curriculum incorporates discussions on research papers, and students also pursue hands-on exercises in machine learning tailored to specific engineering issues including robotics, aircraft, structures, and metamaterials. For their final project, students work together on a team project that employs AI techniques for design on a complex problem of their choice.<\/p>\n

\u201cIt is wonderful to see the diverse breadth and high quality of class projects,\u201d says Ahmed. \u201cStudent projects from this course often lead to research publications, and have even led to awards.\u201d He cites the example of a recent paper, titled \u201cGenCAD-Self-Repairing<\/a>,\u201d that went on to win the American Society of Mechanical Engineers Systems Engineering, Information and Knowledge Management 2025 Best Paper Award.<\/p>\n

\u201cThe best part about the final project was that it gave every student the opportunity to apply what they\u2019ve learned in the class to an area that interests them a lot,\u201d says Malia Smith, a graduate student in MechE. Her project chose \u201cmarkered motion captured data\u201d and looked at predicting ground force for runners, an effort she called \u201creally gratifying\u201d because it worked so much better than expected.<\/p>\n

Lauber took the framework of a \u201ccat tree\u201d design with different modules of poles, platforms, and ramps to create customized solutions for individual cat households, while Moyer created software that is designing a new type of 3D printer architecture.<\/p>\n

\u201cWhen you see machine learning in popular culture, it\u2019s very abstracted, and you have the sense that there\u2019s something very complicated going on,\u201d says Moyer. \u201cThis class has opened the curtains.\u201d\u00a0<\/p>\n","protected":false},"excerpt":{"rendered":"Artificial intelligence optimization offers a host of benefits for mechanical engineers, including faster and more accurate designs and…\n","protected":false},"author":3,"featured_media":206876,"comment_status":"","ping_status":"","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[21],"tags":[691,113735,738,113733,113739,113736,113738,113740,113737,113731,113734,113732,158,67,132,68],"class_list":{"0":"post-206875","1":"post","2":"type-post","3":"status-publish","4":"format-standard","5":"has-post-thumbnail","7":"category-artificial-intelligence","8":"tag-ai","9":"tag-ai-in-mechanical-engineering","10":"tag-artificial-intelligence","11":"tag-design-computation-and-digital-engineering-lab-decode","12":"tag-em-lauber","13":"tag-faez-ahmed","14":"tag-ilan-moyer","15":"tag-lyle-regenwetter","16":"tag-malia-smith","17":"tag-mit-class-2-155-2-156-artificial-intelligence-and-machine-learning-for-engineering-design","18":"tag-mit-mechanical-engineering","19":"tag-mit-meche","20":"tag-technology","21":"tag-united-states","22":"tag-unitedstates","23":"tag-us"},"share_on_mastodon":{"url":"https:\/\/pubeurope.com\/@us\/115161421744878492","error":""},"_links":{"self":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/posts\/206875","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/users\/3"}],"replies":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/comments?post=206875"}],"version-history":[{"count":0,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/posts\/206875\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/media\/206876"}],"wp:attachment":[{"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/media?parent=206875"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/categories?post=206875"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.europesays.com\/us\/wp-json\/wp\/v2\/tags?post=206875"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}