Neural Metamaterial Networks for Nonlinear Material Design
Yue Li,
Stelian Coros,
Bernhard Thomaszewski
Abstract:Nonlinear metamaterials with tailored mechanical properties have applications in engineering, medicine, robotics, and beyond. While modeling their macromechanical behavior is challenging in itself, finding structure parameters that lead to ideal approximation of high-level performance goals is a challenging task. In this work, we propose Neural Metamaterial Networks (NMN)---smooth neural representations that encode the nonlinear mechanics of entire metamaterial families. Given structure parameters as input, NM… Show more
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