首页|Research from Westlake University Broadens Understanding of Machine Learning (On-demand tunable metamaterials design for noise attenuation with machine learning)
Research from Westlake University Broadens Understanding of Machine Learning (On-demand tunable metamaterials design for noise attenuation with machine learning)
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NETL
NSTL
Elsevier
New study results on artificial intelligence have been published. According to news originating from Zhejiang, People’s Republic of China, by NewsRx editors, the research stated, “Metamaterials with structure-dominated properties provide a new way to design structures to obtain desired performance.” Our news reporters obtained a quote from the research from Westlake University: “To achieve a wide range of applications, on-demand tunable metamaterials would fulfill various and changing needs. The design of on-demand tunable metamaterials requires a higher-level understanding of the relationship between the properties of the metamaterials and the geometrical parameters, which in many cases are complicated and implicit. With the advancement of machine learning and evolutionary methods, it becomes possible to design on-demand tunable metamaterials. This paper designs on-demand tunable acoustic metamaterials for noise attenuation at varying frequencies by employing a genetic algorithm based neural network method. The C-shaped acoustic metamaterials with slidable shells are combined with the specifically designed tri-stable origami-inspired metamaterials to realize the on-demand tunable structure. Experiments were conducted and showed that the designed tunable metamaterials exhibited desired characteristics in different targeting frequency ranges.”
Westlake UniversityZhejiangPeople’s Republic of ChinaAsiaCyborgsEmerging TechnologiesMachine Learning
NETL
NSTL
Elsevier
