# Physics-informed neural networks for inverse problems in nano-optics and metamaterials.

@article{Chen2020PhysicsinformedNN, title={Physics-informed neural networks for inverse problems in nano-optics and metamaterials.}, author={Y. Chen and L. Lu and G. E. Karniadakis and L. Dal Negro}, journal={Optics express}, year={2020}, volume={28 8}, pages={ 11618-11633 } }

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… CONTINUE READING

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#### References

##### Publications referenced by this paper.

SHOWING 1-10 OF 73 REFERENCES

Past achievements and future challenges in the development of three-dimensional photonic metamaterials

- Physics
- 2011

- 1,212
- PDF

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

- Mathematics, Computer Science
- 2019

- 482

Plasmonic cloaking of cylinders: Finite length, oblique illumination and cross-polarization coupling

- Physics
- 2010

- 63
- PDF

Efficient and accurate inversion of multiple scattering with deep learning

- Physics, Computer Science
- 2018

- 62
- PDF

Effective medium theory for two-dimensional non-magnetic metamaterial lattices up to quadrupole expansions

- Physics
- 2015

- 4