The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Physics-informed neural networks for inverse problems in nano-optics and metamaterials
Open accessAbstract
In this paper, we employ the emerging paradigm of physics-informed neural networks (PINNs) for the solution of representative inverse scattering problems in photonic metamaterials and nano-optics technologies. In particular, we successfully apply mesh-free PINNs to the difficult task of retrieving the effective permittivity parameters of a number of finite-size scattering systems that involve many interacting nanostructures as well as multi-component nanoparticles. Our methodology is fully validated by numerical simulations based on the finite element method (FEM). The development of physics-informed deep learning techniques for inverse scattering can enable the design of novel functional nanostructures and significantly broaden the design space of metamaterials by naturally accounting for radiation and finite-size effects beyond the limitations of traditional effective medium theories.
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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
Self-citationInverse design in nanophotonics
Molesky, S., Lin, Z., Piggott, A. Y., Jin, W., Vucković, J., & Rodriguez, A. W. (2018) · Nature Photonics · doi:10.1038/s41566-018-0246-9 · Cited by 1,818
Effective medium theory for magnetodielectric composites: Beyond the long-wavelength limit
Wu, Y., Li, J., Zhang, Z.-Q., & Chan, C. T. (2006) · Physical Review B · doi:10.1103/physrevb.74.085111 · Cited by 231
Cited by
Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators
Lu, L., Jin, P., Pang, G., Zhang, Z., & Karniadakis, G. E. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00302-5 · Cited by 3,418
Self-citationGradient-enhanced physics-informed neural networks for forward and inverse PDE problems
Yu, J., Lu, L., Meng, X., & Karniadakis, G. E. (2022) · Computer Methods in Applied Mechanics and Engineering · doi:10.1016/j.cma.2022.114823 · Cited by 722
Self-citationEmpowering Metasurfaces with Inverse Design: Principles and Applications
Li, Z., Pestourie, R., Lin, Z., Johnson, S. G., & Capasso, F. (2022) · ACS Photonics · doi:10.1021/acsphotonics.1c01850 · Cited by 283
Neural reparameterization for nonlocal metasurface topology optimization
Clark, L., Roberts, A., & Wesemann, L. (2025) · APL Photonics · doi:10.1063/5.0283122 · Cited by 4
Metadata
- Authors
- Chen, Y., Lu, L., Karniadakis, G. E., & Dal Negro, L.
- Venue
- Optics Express
- Publisher
- Optica Publishing Group
- Published
- 2020-04-06
- Volume
- 28
- Issue
- 8
- Pages
- 11618
- ISSN
- 1094-4087 (ISSN-L)
- OpenAlex
- W2993158195
- Cited by
- 701
- References
- 3
- FWCI
- 39.64
- Type
- journal article
- Language
- en
- Open access
- gold
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- Chen, Y., Lu, L., Karniadakis, G. E., & Dal Negro, L. (2020). Physics-informed neural networks for inverse problems in nano-optics and metamaterials. Optics Express. https://doi.org/10.1364/oe.384875
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