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IMAGE: This visualization shows the deep-learning approach in predicting physical fields given different input geometries. The left figure shows a varying geometry of the composite in which the soft material is. view more
Credit: Courtesy of Zhenze Yang, Markus Buehler, et al
Isaac Newton may have met his match.
For centuries, engineers have relied on physical laws developed by Newton and others to understand the stresses and strains on the materials they work with. But solving those equations can be a computational slog, especially for complex materials.
MIT researchers have developed a technique to quickly determine certain properties of a material, like stress and strain, based on an image of the material showing its internal structure. The approach could one day eliminate the need for arduous physics-based calculations, instead relying on computer vision and machine learning to generate estimates in real time.
Credits: Courtesy of the researchers
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Isaac Newton may have met his match.
For centuries, engineers have relied on physical laws developed by Newton and others to understand the stresses and strains on the materials they work with. But solving those equations can be a computational slog, especially for complex materials.
MIT researchers have developed a technique to quickly determine certain properties of a material, like stress and strain, based on an image of the material showing its internal structure. The approach could one day eliminate the need for arduous physics-based calculations, instead relying on computer vision and machine learning to generate estimates in real time.