Engineering and TechnologyScientific Reports
Design of optical meta-structures with applications to beam engineering using deep learning
R. Singh, A. Agarwal, et al.
This groundbreaking research by Robin Singh, Anu Agarwal, and Brian W. Anthony delves into an innovative machine learning approach to reverse engineer meta-optical structures. It harnesses data-driven techniques to generate focused and collimated excitation beams for cutting-edge lab-on-chip applications, achieving remarkable accuracy with a high correlation coefficient in predicting diffraction profiles, significantly outpacing traditional optimization methods.
Related Publications
Explore these studies to deepen your understanding
Adjacent work that informs or extends this paper's methodology and findings.
Medicine and Health
Design and Analysis of a Deep Learning Ensemble Framework Model for the Detection of COVID-19 and Pneumonia Using Large-Scale CT Scan and X-ray Image Datasets
X. Xue, S. Chinnaperumal, et al.
Chemistry
Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations
S. Chen, T. Lin, et al.
Medicine and Health
Automating General Movements Assessment with quantitative deep learning to facilitate early screening of cerebral palsy
Q. Gao, S. Yao, et al.
Chemistry
Representation of molecular structures with persistent homology for machine learning applications in chemistry
J. Townsend, C. P. Micucci, et al.

