The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Deep Physical Informed Neural Networks for Metamaterial Design
Open accessAbstract
In this paper, we propose a physical informed neural network approach for designing the electromagnetic metamaterial. The approach can be used to deal with various practical problems such as cloaking, rotators, concentrators, etc. The advantage of this approach is the flexibility that we can deal with not only the continuous parameters but also the piecewise constants. As our best knowledge, there is no other faster and much efficient method to deal with these problems. As a byproduct, we propose a method to solve high frequency Helmholtz equation, which is widely used in physics and engineering. Some benchmark problems have been solved in numerical tests to verify our method.
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Cites
Raissi, M., Perdikaris, P., & Karniadakis, G. E. (2019) · Journal of Computational Physics · doi:10.1016/j.jcp.2018.10.045 · Cited by 20,384
Negative Refraction Makes a Perfect Lens
Pendry, J. B. (2000) · Physical Review Letters · doi:10.1103/physrevlett.85.3966 · Cited by 12,159
Mathematical Biology: II: Spatial Models and Biomedical Applications
(2003) · Interdisciplinary Applied Mathematics · doi:10.1007/b98869 · Cited by 2,293
Deep Learning in Drug Discovery
Gawehn, E., Hiss, J. A., & Schneider, G. (2016) · Molecular Informatics · doi:10.1002/minf.201501008 · Cited by 723
Metadata
- Authors
- Fang, Z., & Zhan, J.
- Venue
- IEEE Access
- Published
- 2020
- Volume
- 8
- Pages
- 24506-24513
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2998004632
- Cited by
- 174
- References
- 4
- FWCI
- 3.08
- Type
- journal article
- Language
- en
- Open access
- gold
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- Fang, Z., & Zhan, J. (2020). Deep Physical Informed Neural Networks for Metamaterial Design. IEEE Access. https://doi.org/10.1109/access.2019.2963375
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