Results 211 to 220 of about 2,038,050 (333)

Deep Learning Integration in Optical Microscopy: Advancements and Applications

open access: yesMicroscopy Research and Technique, EarlyView.
It explores the integration of DL into optical microscopy, focusing on key applications including image classification, segmentation, and computational reconstruction. ABSTRACT Optical microscopy is a cornerstone imaging technique in biomedical research, enabling visualization of subcellular structures beyond the resolution limit of the human eye ...
Pottumarthy Venkata Lahari   +5 more
wiley   +1 more source

Engineering Topological Phases with a Traveling‐Wave Spacetime Modulation

open access: yesLaser &Photonics Reviews, EarlyView.
Time‐varying systems have garnered considerable attention due to their unique potential in manipulating electromagnetic waves. Here, a novel class of topological spacetime crystals with a traveling‐wave modulation is introduced. By manipulating material anisotropy, one can engineer light angular momentum and non‐trivial topological phases characterized
João C. Serra, Mário G. Silveirinha
wiley   +1 more source

Using Adaptive Surrogate Models to Accelerate Multi-Objective Design Optimization of MEMS. [PDF]

open access: yesMicromachines (Basel)
Nazari A   +5 more
europepmc   +1 more source

Approximation of Dirac operators with δ‐shell potentials in the norm resolvent sense, II: Quantitative results

open access: yesMathematische Nachrichten, EarlyView.
Abstract This paper is devoted to the approximation of two‐ and three‐dimensional Dirac operators HV∼δΣ$H_{\widetilde{V} \delta _\Sigma }$ with combinations of electrostatic and Lorentz scalar δ$\delta$‐shell interactions in the norm resolvent sense. Relying on results from Behrndt, Holzmann, and Stelzer‐Landauer [Math. Nachr.
Jussi Behrndt   +2 more
wiley   +1 more source

Predicting Infrared Optical Properties of Materials Using Machine Learning Interatomic Potentials

open access: yesMaterials Genome Engineering Advances, EarlyView.
This work proposes a new fast computing framework for infrared reflectance spectra, MTP‐FIRE, based on machine learning potential, which can achieve the same accuracy as the existing first‐principles calculation, but can be two orders of magnitude faster on average.
Lianduan Zeng   +8 more
wiley   +1 more source

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