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Metamaterial-based realization for thermal transparency: A conditional variational autoencoder approach.
- Source :
-
Physica B . Jul2024, Vol. 684, pN.PAG-N.PAG. 1p. - Publication Year :
- 2024
-
Abstract
- The advances in thermal metamaterials and their applications have revolutionized how we can manipulate thermal transport behavior. The challenging inverse design problems of utilizing thermal metamaterial-based structures to achieve desired thermal transport behavior are increasingly being tackled by data-driven, machine learning-based approaches. The explosive progress in generative AI is permeating the field of material design by offering new perspectives to address the inverse design problems. In this paper, we propose a simple yet effective method of training a generative conditional variational autoencoder to find the design parameters for a thermal metamaterial-based system with a periodic interparticle arrangement to achieve thermal transparency, which is one of the most desirable and interesting thermal transport behaviors. Our work attests to the predictive power of a generative model with a relatively small number of parameters for the purpose of tackling inverse design problems to achieve thermal transport behavior manipulation. • Thermal metamaterials empower us to manipulate thermal transport behavior. • Generative AI models are powerful tools to address the inverse design problems. • Conditional variational encoder efficiently addresses the inverse design problems. • The obtained metamaterial-based structure achieves thermal transparency. [ABSTRACT FROM AUTHOR]
- Subjects :
- *GENERATIVE artificial intelligence
*INVERSE problems
*METAMATERIALS
Subjects
Details
- Language :
- English
- ISSN :
- 09214526
- Volume :
- 684
- Database :
- Academic Search Index
- Journal :
- Physica B
- Publication Type :
- Academic Journal
- Accession number :
- 176991341
- Full Text :
- https://doi.org/10.1016/j.physb.2024.415975