Machine Learning Aided Design and Optimization of Thermal Metamaterials
Southern University of Science and Technology · University of Colorado Boulder
Abstract
Artificial Intelligence (AI) has advanced material research that were previously intractable, for example, the machine learning (ML) has been able to predict some unprecedented thermal properties. In this review, we first elucidate the methodologies underpinning discriminative and generative models, as well as the paradigm of optimization approaches. Then, we present a series of case studies showcasing the application of machine learning in thermal metamaterial design. Finally, we give a brief discussion on the challenges and opportunities in this fast developing field. In particular, this review provides: (1) Optimization of thermal metamaterials using optimization algorithms to achieve specific target…
Citation impact
- FWCI
- 34.70
- Percentile
- 100%
- References
- 490
Authors
5- CZChangliang Zhu
Southern University of Science and Technology
- EAEmmanuel Anuoluwa Bamidele
University of Colorado Boulder
- XSXiangying ShenCorresponding
Southern University of Science and Technology
- GZGuimei ZhuCorresponding
Southern University of Science and Technology
- BLBaowen LiCorresponding
University of Colorado Boulder, Southern University of Science and Technology
Topics & keywords
- Metamaterial
- Discriminative model
- Generative Design
- Generative grammar
- Computer science
- Field (mathematics)
- Topology optimization
- Artificial intelligence
- Reduced inequalities