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Recently, deep learning has yielded transformative success across optics and photonics, especially in optical metrology. Deep neural networks (DNNs) with a fully convolutional architecture (e.g., U-Net and its derivative...

Vegetation optical depth (VOD), as a key parameter of vegetation, plays an important role in the process of vegetation growth and change. The spaceborne Global Navigation Satellite System Reflectometry (GNSS-R) technolog...

Abstract Accurate estimation of the shear capacity of steel fiber reinforced concrete (SFRC) beams without stirrups remains challenging because the response is governed by strongly coupled geometric, material, and reinfo...

On-chip computing metasystems composed of multilayer metamaterials have the potential to become the next-generation computing hardware endowed with light-speed processing ability and low power consumption but are hindere...

Abstract Alkaline water electrolysis represents a promising pathway for green hydrogen production, yet comprehensive multi-physics simulation remains computationally prohibitive for practical design optimization. This st...

Deep learning (DL) is making significant inroads into biomedical imaging as it provides novel and powerful ways of accurately and efficiently improving the image quality of photoacoustic microscopy (PAM). Off-the-shelf D...

Abstract Accurate prediction of terrestrial water cycle variables like soil moisture (SM) and evapotranspiration (ET) is crucial, yet existing Deep Learning (DL) models face limitations. Many adopt Single-Task Learning (...

The functional asymmetry of the human brain hemispheres (motor, sensory, and mental) reflects the differences in the distribution of neuro-psychic functions between the left and right hemispheres. The left hemisphere is ...

Introduction We developed an innovative, active learning workshop to introduce foundations of point-of-care ultrasound (POCUS) physics and instrumentation. The workshop design aimed to reduce cognitive load and encourage...

Real-time vehicle prediction is crucial in autonomous driving technology, as it allows adjustments to be made in advance to the driver or the vehicle, enabling them to take smoother driving actions to avoid potential col...

To fully exploit the advantages of GNSS-R small satellites (large quantity, short revisit cycle) and address key challenges of multisource GNSS-R data—track-wise distribution, uneven spatial coverage, and observat...

Machine-Learned Likelihood (MLL) is a method that, by combining modern machine-learning techniques with likelihood-based inference tests, allows estimating the experimental sensitivity of high-dimensional data sets. Here...

Additive manufacturing has become an important tool for fabricating advanced systems and devices for visible nanophotonics. However, the lack of simulation and optimization methods taking into account the essential physi...

In the present work we carried out a qualitative educational research to assess the perceptions of a group of students that attended (weekly and non-mandatory) complementary classes of a physics course during 2017. In th...

Car-following models (CFMs) are fundamental to traffic flow analysis and autonomous driving. Although calibrated physics-based and trained data-driven CFMs can replicate human driving behavior, their reliance on specific...

Purpose – This study critically examines the rapid expansion of artificial intelligence (AI) in digital learning, with a specific focus on how these technologies intersect with metacognitive skills and digital literacy...

Deep learning has become the dominant approach in artificial intelligence to solve complex data-driven problems. Originally applied almost exclusively in computer-science areas such as image analysis and nature language ...

Optical tweezers exploit light–matter interactions to trap particles ranging from single atoms to micrometer-sized eukaryotic cells. For this reason, optical tweezers are a ubiquitous tool in physics, biology, and nano...

Photonics inverse design relies on human experts to search for a design topology that satisfies certain optical specifications with their experience and intuitions, which is relatively labor-intensive, slow, and sub-opti...

The identification of exoplanets within habitable zones remains a central objective in modern astrophysics, particularly with the availability of large-scale photometric datasets from space-based missions such as the Tra...

The exponential growth of scientific literature on platforms such as arXiv presents a major challenge in identifying and comparing key contributions to machine learning across diverse academic domains. To address this, w...

Abstract Ultrasonic testing (UT) is increasingly combined with machine learning (ML) techniques for intelligently identifying damage. Extracting significant features from UT data is essential for efficient defect charact...

We demonstrate that a software architecture using innovations in machine learning and adaptive control provides an ideal integration platform for self-tuning optics. For mode-locked lasers, commercially availab...

In the recent years, a dramatic boost of the research is observed at the junction of photonics, machine learning and artificial intelligence. A new methodology can be applied to the description of a variety of photonic s...