| Machine learning approach for computing optical properties of a photonic crystal fiber S Chugh, A Gulistan, S Ghosh, BMA Rahman Optics express 27 (25), 36414-36425, 2019 | 153 | 2019 |
| Machine learning regression approach to the nanophotonic waveguide analyses S Chugh, S Ghosh, A Gulistan, BMA Rahman Journal of Lightwave Technology 37 (24), 6080-6089, 2019 | 60 | 2019 |
| Artificial neural network modelling for optimizing the optical parameters of plasmonic paired nanostructures S Verma, S Chugh, S Ghosh, BMA Rahman Nanomaterials 12 (1), 170, 2022 | 12 | 2022 |
| Deep learning based data augmentation and behavior prediction of photonic crystal fiber temperature sensor S Sridevi, T Kanimozhi, N Ayyanar, S Chugh, M Valliammai, J Mohanraj IEEE Sensors Journal 22 (7), 6832-6839, 2022 | 8 | 2022 |
| A comprehensive deep learning method for empirical spectral prediction and its quantitative validation of nano-structured dimers S Verma, S Chugh, S Ghosh, BMA Rahman Scientific Reports 13 (1), 1129, 2023 | 7 | 2023 |
| Air-holes induced multimodal fiber design to increase the effective index difference between higher order guided modes A Gulistan, S Ghosh, S Chugh, BMA Rahman Optical Fiber Technology 53, 102023, 2019 | 1 | 2019 |
| High-index contrast grating based broadband out-coupler on SOI S Chugh, M Kumar Optik 124 (20), 4164-4166, 2013 | 1 | 2013 |
| Machine learning modelling, optimisation and thermal compensation of photonic waveguides S Chugh City, University of London, 2020 | | 2020 |
| Modelling of Hybrid Plasmonic Asymmetric Mach-Zehnder Interferometer for Sensing Applications S Chugh, S Ghosh, BMA Rahman Photonics-2018, IIT Delhi, India, 2018 | | 2018 |
| Enhancing the Sensitivity by using Split Sections in a Ring Resonator S Chugh, S Ghosh, BMA Rahman Photonics-2018, IIT Delhi, India, 2018 | | 2018 |
Azizur RahmanCity University LondonVerified email at city.ac.uk
