Pages that link to "Item:Q2022061"
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The following pages link to Computational design of innovative mechanical metafilters via adaptive surrogate-based optimization (Q2022061):
Displaying 11 items.
- Computational design of locally resonant acoustic metamaterials (Q1986770) (← links)
- Convex combination of data matrices: PCA perturbation bounds for multi-objective optimal design of mechanical metafilters (Q2065508) (← links)
- Inverse design of shell-based mechanical metamaterial with customized loading curves based on machine learning and genetic algorithm (Q2096817) (← links)
- Data-driven approach for dynamic homogenization using meta learning (Q2096905) (← links)
- Inverse design of locally resonant metabarrier by deep learning with a rule-based topology dataset (Q2136755) (← links)
- Anisotropic peridynamics for homogenized microstructured materials (Q2138722) (← links)
- Structural optimization of metamaterials based on periodic surface modeling (Q2142217) (← links)
- Designing phononic crystal with anticipated band gap through a deep learning based data-driven method (Q2176922) (← links)
- Predicting band structure of 3D mechanical metamaterials with complex geometry via XFEM (Q2341506) (← links)
- Optimised graded metamaterials for mechanical energy confinement and amplification via reinforcement learning (Q2692865) (← links)
- Deep learning of dispersion engineering in two-dimensional phononic crystals (Q6048498) (← links)
