| Restricted Boltzmann machines for quantum states with non-Abelian or anyonic symmetries T Vieijra, C Casert, J Nys, W De Neve, J Haegeman, J Ryckebusch, ... Physical review letters 124 (9), 097201, 2020 | 95* | 2020 |
| Interpretable machine learning for inferring the phase boundaries in a nonequilibrium system C Casert, T Vieijra, J Nys, J Ryckebusch Physical Review E 99 (2), 023304, 2019 | 46 | 2019 |
| The isospin and neutron-to-proton excess dependence of short-range correlations J Ryckebusch, W Cosyn, S Stevens, C Casert, J Nys Physics Letters B 792, 21-28, 2019 | 26 | 2019 |
| Social stability and extended social balance—Quantifying the role of inactive links in social networks AM Belaza, J Ryckebusch, A Bramson, C Casert, K Hoefman, K Schoors, ... Physica A: Statistical Mechanics and its Applications 518, 270-284, 2019 | 25 | 2019 |
| Dynamical large deviations of two-dimensional kinetically constrained models using a neural-network state ansatz C Casert, T Vieijra, S Whitelam, I Tamblyn Physical review letters 127 (12), 120602, 2021 | 23 | 2021 |
| Isospin composition of the high-momentum fluctuations in nuclei from asymptotic momentum distributions J Ryckebusch, W Cosyn, T Vieijra, C Casert Physical Review C 100 (5), 054620, 2019 | 20 | 2019 |
| Robust prediction of force chains in jammed solids using graph neural networks R Mandal, C Casert, P Sollich Nature Communications 13 (1), 4424, 2022 | 12 | 2022 |
| Optical lattice experiments at unobserved conditions with generative adversarial deep learning C Casert, K Mills, T Vieijra, J Ryckebusch, I Tamblyn Physical Review Research 3 (3), 033267, 2021 | 11 | 2021 |
| Training neural networks using Metropolis Monte Carlo and an adaptive variant S Whitelam, V Selin, I Benlolo, C Casert, I Tamblyn Machine Learning: Science and Technology 3 (4), 045026, 2022 | 9 | 2022 |
| Adversarial generation of mesoscale surfaces from small-scale chemical motifs K Mills, C Casert, I Tamblyn The Journal of Physical Chemistry C 124 (42), 23158-23163, 2020 | 8 | 2020 |
| Learning stochastic dynamics and predicting emergent behavior using transformers C Casert, I Tamblyn, S Whitelam Nature Communications 15 (1), 1875, 2024 | 4 | 2024 |
| Learning protocols for fast and efficient state-to-state transformations in active matter S Whitelam, C Casert Bulletin of the American Physical Society, 2024 | | 2024 |
| Learning protocols for the fast and efficient control of active matter C Casert, S Whitelam arXiv preprint arXiv:2402.18823, 2024 | | 2024 |
| Revealing nonequilibrium phenomena and slow dynamics in many-body systems through machine learning C Casert Ghent University, 2023 | | 2023 |
| Towards neural network quantum states with nonabelian symmetries T Vieijra, C Casert, J Nys, W De Neve, J Haegeman, J Ryckebusch, ... Bulletin of the American Physical Society 65, 2020 | | 2020 |
| Adversarial machine learning for modeling the distribution of large-scale ultracold atom experiments C Casert, K Mills, T Vieijra, J Ryckebusch, I Tamblyn Bulletin of the American Physical Society 65, 2020 | | 2020 |
| Discriminative and generative machine learning for spin systems based on physically interpretable features C Casert, K Mills, J Nys, J Ryckebusch, I Tamblyn, T Vieijra StatPhys 27 Main Conference, 2019 | | 2019 |
| Dynamical large deviations of kinetically constrained models using neural-network states C Casert, T Vieijra, S Whitelam, I Tamblyn | | |
| Large deviations of one-dimensional kinetically constrained models with recurrent neural networks C Casert, T Vieijra, S Whitelam, I Tamblyn | | |
