Deep learning-based design of ternary metamaterials for isolating full-mode waves. (15th February 2023)
- Record Type:
- Journal Article
- Title:
- Deep learning-based design of ternary metamaterials for isolating full-mode waves. (15th February 2023)
- Main Title:
- Deep learning-based design of ternary metamaterials for isolating full-mode waves
- Authors:
- Liu, Chen-Xu
Yu, Gui-Lan - Abstract:
- Highlights: Highly efficient design of ternary metamaterial is realized. Metamaterials for full-mode waves are customized under different site conditions. Topologies and periodic constants are designed simultaneously. A variational autoencoder and a series–parallel neural network are constructed. The accuracy, stability, generality, and feasibility of the proposed method are verified. Abstract: A deep learning-based methodology is presented to design the topologies and periodic constants of ternary metamaterials under different site conditions, considering both in-plane and anti-plane waves (full-mode waves) coming together. A variational autoencoder (VAE) and a series–parallel neural network (SPNN) are constructed, based on which the design model is established and the metamaterial customization is realized. One thousand designs are performed, where the determination coefficient reaches 0.982, the root mean square error is only 0.785, and the mean design error is 3.1%, evaluating the accuracy and stability of the deep learning method. To illustrate the generality of the presented method, metamaterials under different site conditions are designed. For the same target, multiple sets of metamaterials are given, which reflects the non-uniqueness of design problems. Two sets of metamaterials respectively for two practical examples are customized and discussed with 3D finite element models, verifying their ability of isolating vibrations in all directions.
- Is Part Of:
- Engineering structures. Volume 277(2023)
- Journal:
- Engineering structures
- Issue:
- Volume 277(2023)
- Issue Display:
- Volume 277, Issue 2023 (2023)
- Year:
- 2023
- Volume:
- 277
- Issue:
- 2023
- Issue Sort Value:
- 2023-0277-2023-0000
- Page Start:
- Page End:
- Publication Date:
- 2023-02-15
- Subjects:
- Metamaterials -- Local resonance -- Deep learning -- Inverse design -- Vibration isolation -- Bandgap -- Periodic structures
Structural engineering -- Periodicals
Structural analysis (Engineering) -- Periodicals
Construction, Technique de la -- Périodiques
Génie parasismique -- Périodiques
Pression du vent -- Périodiques
Earthquake engineering
Structural engineering
Wind-pressure
Periodicals
624.105 - Journal URLs:
- http://www.sciencedirect.com/science/journal/01410296 ↗
http://www.elsevier.com/journals ↗ - DOI:
- 10.1016/j.engstruct.2022.115441 ↗
- Languages:
- English
- ISSNs:
- 0141-0296
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3770.032000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 25942.xml