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Found 10 papers, 2 papers with code
Subwavelength Imaging using a Solid-Immersion Diffractive Optical Processor
To resolve subwavelength features of an object, the diffractive imager uses a thin, high-index solid-immersion layer to transmit high-frequency information of the object to a spatially-optimized diffractive encoder, which converts/encodes high-frequency information of the input into low-frequency spatial modes for transmission through air.
Nonlinear Processing with Linear Optics
In this study, we present a novel framework that uses multiple scattering that is capable of synthesizing programmable linear and nonlinear transformations concurrently at low optical power by leveraging the nonlinear relationship between the scattering potential, represented by data, and the scattered field.
Forward-Forward Training of an Optical Neural Network
Neural networks (NN) have demonstrated remarkable capabilities in various tasks, but their computation-intensive nature demands faster and more energy-efficient hardware implementations.
Nonlinear Optical Data Transformer for Machine Learning
A dataset is encoded digitally on the spectrum of a femtosecond pulse which is then launched in the waveguide.
Physics-informed neural networks for diffraction tomography
Our physics-informed neural networks can be generalized for any forward and inverse scattering problem.
Optical Diffraction Tomography based on 3D Physics-Inspired Neural Network (PINN)
The 3D NN starts with an initial guess for the 3D RI reconstruction (i. e. Born, or Rytov) and aims at reconstructing better 3D reconstruction based on an error function.
Variational framework for partially-measured physical system control: examples of vision neuroscience and optical random media
To characterize a physical system to behave as desired, either its underlying governing rules must be known a priori or the system itself be accurately measured.
Scalable Optical Learning Operator
Today's heavy machine learning tasks are fueled by large datasets.
Competing Neural Networks for Robust Control of Nonlinear Systems
The output of physical systems is often accessible by measurements such as the 3D position of a robotic arm actuated by many actuators or the speckle patterns formed by shining the spot of a laser pointer on a wall.
Deep-learning PDEs with unlabeled data and hardwiring physics laws
Providing fast and accurate solutions to partial differential equations is a problem of continuous interest to the fields of applied mathematics and physics.
