首页|Researchers from Georgia Institute of Technology Describe Findings in Machine Le arning (Machine Learning and Sequential Subdomain Optimization for Ultrafast Inv erse Design of 4d-printed Active Composite Structures)
Researchers from Georgia Institute of Technology Describe Findings in Machine Le arning (Machine Learning and Sequential Subdomain Optimization for Ultrafast Inv erse Design of 4d-printed Active Composite Structures)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on Machine Learning is now available. According to news reporting out of Atlanta, Georgia, by NewsRx edito rs, research stated, “Shape transformations of active composites (ACs) depend on the spatial distribution and active response of constituent materials. Voxel-le vel complex material distributions offer a vast possibility for attainable shape changes of 4D-printed ACs, while also posing a significant challenge in efficie ntly designing material distributions to achieve target shape changes.” Financial supporters for this research include Air Force Office of Scientific Re search (AFOSR), HP, Inc.
AtlantaGeorgiaUnited StatesNorth a nd Central AmericaCyborgsEmerging TechnologiesMachine LearningGeorgia In stitute of Technology
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