We will describe MaxwellNet, a machine learning method for analyzing and designing optical systems using Maxwell's equations as the loss function in the optimisation process. We will describe results of the application of MaxwellNet to nanophotonics, nonlinear optics and optical diffraction tomography
Deep neural network trained on physical losses are emerging as promising surrogates of nonlinear numerical solvers. These tools can predict solutions of Maxwell’s equations and compute gradients of output fields with respect to material properties in millisecond times which makes them very attractive for inverse design or inverse scattering applications. Here we demonstrate a neural network able to compute light scattering from inhomogeneous media in the presence of the optical Kerr effect from glass diffusers with a size comparable with the incident wavelength. The weights of the network are dynamically adjusted to take into account the intensity dependent refractive index of the material.
We propose a physics-informed neural network (PINN) as the forward model for tomographic reconstructions of biological samples. We demonstrate that by training this network with the Helmholtz equation as a physical loss, we can predict the scattered field accurately. It will be shown that a pretrained network can be fine-tuned for different samples and used for solving the scattering problem much faster than other numerical solutions. We evaluate our methodology with numerical and experimental results. Our PINNs can be generalized for any forward and inverse scattering problem.
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