Source Themes

Using Deep Neural Networks to Generate Hierarchical Metamaterials for Enhanced Mechanical Properties

Hierarchical metamaterials with particularly designed microstructure may exhibit unconventional mechanical properties, such as chiral effects, negative Poisson’s ratio (NPR) or negative thermal expansion coefficient (NTEC). Due to the internal …

Optimization of Chiral Metamaterials via Deep Neural Networks

Metamaterials with particularly designed microstructure may exhibit unconventional physical properties, such as negative index of refraction (NIR), negative Poisson’s ratio (NPR) or negative thermal expansion coefficient (NTEC). By introducing …

Design of Chiral Metamaterials via Deep Neural Networks

Evaluate the use of DNN models in a self-updating material design process.

Machine learning of viscoelastic properties of 2D porous materials via deep neural network (In Preperation)

Discussed the use of VGG networks as an alternative of Finite Element Methods (FEM) when labeling mechanical properties of small size 2D microstructure geometries.

Symmetric Quantum Walk With Phase Transition Feature (In preperation)

Observations and analysis of one-dimention multiple way quantum walk.