| A newcomer’s guide to deep learning for inverse design in nano-photonics A Khaireh-Walieh, D Langevin, P Bennet, O Teytaud, A Moreau, ... Nanophotonics 12 (24), 4387-4414, 2023 | 30 | 2023 |
| Inverse design with flexible design targets via deep learning: Tailoring of electric and magnetic multipole scattering from nano-spheres A Estrada-Real, A Khaireh-Walieh, B Urbaszek, PR Wiecha Photonics and Nanostructures-Fundamentals and Applications 52, 101066, 2022 | 17 | 2022 |
| Monitoring MBE substrate deoxidation via RHEED image-sequence analysis by deep learning A Khaireh-Walieh, A Arnoult, S Plissard, PR Wiecha Crystal Growth & Design 23 (2), 892-898, 2023 | 11 | 2023 |
| Illustrated tutorial on global optimization in nanophotonics P Bennet, D Langevin, C Essoual, A Khaireh-Walieh, O Teytaud, ... Journal of the Optical Society of America B 41 (2), A126-A145, 2024 | 8 | 2024 |
| PyMoosh: a comprehensive numerical toolkit for computing the optical properties of multilayered structures D Langevin, P Bennet, A Khaireh-Walieh, P Wiecha, O Teytaud, A Moreau Journal of the Optical Society of America B 41 (2), A67-A78, 2024 | 5 | 2024 |
| Deep Learning for nanotechnology: crystal growth characterization and nano-photonics inverse design A Khaireh-Walieh UPS Toulouse-Université Toulouse 3 Paul Sabatier, 2024 | | 2024 |
