| Artificial neural networks for inverse design of resonant nanophotonic components with oscillatory loss landscapes J Lenaerts, H Pinson, V Ginis Nanophotonics 10 (1), 385-392, 2020 | 16 | 2020 |
| Linear CNNs discover the statistical structure of the dataset using only the most dominant frequencies H Pinson, J Lenaerts, V Ginis International Conference on Machine Learning, 27876-27906, 2023 | 1 | 2023 |
| Questioning the question: Exploring how physical degrees of freedom are retrieved with neural networks J Lenaerts, V Ginis Physical Review Research 4 (2), 023206, 2022 | 1 | 2022 |
| Towards the ideal emitter: an inverse-designed metasurface for radiative cooling J Lenaerts, V Ginis Metamaterials XIV 12568, 125680M, 2023 | | 2023 |
| A beta-variational autoencoder (beta-VAE) to retrieve relevant degrees of freedom in a physical system J Lenaerts, V Ginis Applications of Machine Learning 2022 12227, 63-70, 2022 | | 2022 |
| A physics-inspired approach to overcome oscillatory loss landscapes in the inverse design of optical components J Lenaerts, H Pinson, V Ginis AI and Optical Data Sciences III 12019, 163-172, 2022 | | 2022 |
| Data driven design of optical resonators J Lenaerts, H Pinson, V Ginis arXiv preprint arXiv:2202.03578, 2021 | | 2021 |
| Deep learning the design of optical components J Lenaerts, H Pinson, V Ginis Emerging Topics in Artificial Intelligence 2020 11469, 1146909, 2020 | | 2020 |
| Supplemental Information: Artificial neural networks for inverse design of resonant nanophotonic components with oscillatory loss landscapes J Lenaerts, H Pinson, V Ginis | | |
Vincent GinisVrije Universiteit Brussel / Harvard UniversityVerified email at seas.harvard.edu
