Recent years have seen the rapid growth and development of the field of smart photonics,
where machine-learning algorithms are being matched to optical systems to add new …

In recent years, machine learning, especially various deep neural networks, as an emerging
technique for data analysis and processing, has brought novel insights into the development …

Reservoir computing offers a powerful neuromorphic computing architecture for
spatiotemporal signal processing. To boost the power efficiency of the hardware …

The last decade has seen the development of a wide set of tools, such as wavefront
shaping, computational or fundamental methods, that allow us to understand and control …

The energy conservation law is introduced into a loss function of the physics-informed
neural network (PINN), and an energy-conservation deep-learning (ECDL) method is …

In healthcare, medical image processing is considered one of the most significant
procedures used in diagnosing pathological conditions. Magnetic resonance imaging (MRI) …

Harnessing pulse generation from an ultrafast laser is a challenging task as reaching a
specific mode‐locked regime generally involves adjusting multiple control parameters, in …

Structured waves are ubiquitous for all areas of wave physics, both classical and quantum,
where the wavefields are inhomogeneous and cannot be approximated by a single plane …

Integrating the information of the first cycle of an optical pulse in a cavity into the input of a
neural network, a bidirectional long short-term memory (Bi_LSTM) recurrent neural network …

A physics‐informed neural network (PINN) that combines deep learning with physics is
studied to solve the nonlinear Schrödinger equation for learning nonlinear dynamics in fiber …