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Technique could efficiently solve partial differential equations for numerous applications

MIT researchers propose a "physics-enhanced deep-surrogate" (PEDS) method for developing data-driven surrogate models for complex physical systems in such fields as mechanics, optics, thermal transport, fluid dynamics, physical chemistry, and climate modeling. ....

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Image recognition accuracy: An unseen challenge confounding today's AI | MIT News | Massachusetts Institute of Technology

A novel dataset metric, minimum viewing time (MVT), gauges image recognition complexity for AI systems by measuring the time needed for accurate human identification. ....

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Students pitch transformative ideas in generative AI at MIT Ignite competition

At the first-ever MIT Ignite: Generative AI Entrepreneurship Competition, 12 teams of MIT students and postdocs presented their ideas for startups that utilize generative AI technologies to develop solutions across a diverse range of disciplines. ....

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To excel at engineering design, generative AI must learn to innovate, study finds

A new study reveals the pitfalls of deep generative models when they are tasked with solving engineering design problems. The MIT researchers say if mechanical engineers want help from AI for novel ideas and designs, they’ll have to refocus those models beyond “statistical similarity.” ....

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When computer vision works more like a brain, it sees more like people do

Scientists from MIT and IBM Research made a computer vision model more robust by training it to work like a part of the brain that humans and other primates rely on for object recognition. ....

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