| Array programming with NumPy CR Harris, KJ Millman, SJ Van Der Walt, R Gommers, P Virtanen, ... Nature 585 (7825), 357-362, 2020 | 17576 | 2020 |
| mwaskom/seaborn: v0. 11.2 (August 2021) M Waskom, M Gelbart, O Botvinnik, J Ostblom, P Hobson, S Lukauskas, ... Zenodo, 2021 | 1035* | 2021 |
| Xarray: N-D labeled arrays and datasets in Python S Hoyer, J Hamman Journal of Open Research Software 5 (1), 2017 | 983 | 2017 |
| Machine learning–accelerated computational fluid dynamics D Kochkov, JA Smith, A Alieva, Q Wang, MP Brenner, S Hoyer Proceedings of the National Academy of Sciences 118 (21), e2101784118, 2021 | 802 | 2021 |
| Learning data-driven discretizations for partial differential equations Y Bar-Sinai, S Hoyer, J Hickey, MP Brenner Proceedings of the National Academy of Sciences 116 (31), 15344-15349, 2019 | 571 | 2019 |
| Lagrangian neural networks M Cranmer, S Greydanus, S Hoyer, P Battaglia, D Spergel, S Ho arXiv preprint arXiv:2003.04630, 2020 | 479 | 2020 |
| Learning skillful medium-range global weather forecasting R Lam, A Sanchez-Gonzalez, M Willson, P Wirnsberger, M Fortunato, ... Science 382 (6677), 1416-1421, 2023 | 457* | 2023 |
| The cramer distance as a solution to biased wasserstein gradients MG Bellemare, I Danihelka, W Dabney, S Mohamed, ... arXiv preprint arXiv:1705.10743, 2017 | 414 | 2017 |
| Free-form diffractive metagrating design based on generative adversarial networks J Jiang, D Sell, S Hoyer, J Hickey, J Yang, JA Fan ACS nano 13 (8), 8872-8878, 2019 | 327 | 2019 |
| Efficient and modular implicit differentiation M Blondel, Q Berthet, M Cuturi, R Frostig, S Hoyer, F Llinares-López, ... Advances in neural information processing systems 35, 5230-5242, 2022 | 217 | 2022 |
| Limits of quantum speedup in photosynthetic light harvesting S Hoyer, M Sarovar, KB Whaley New Journal of Physics 12 (6), 065041, 2010 | 200 | 2010 |
| Assessing microscope image focus quality with deep learning SJ Yang, M Berndl, D Michael Ando, M Barch, A Narayanaswamy, ... BMC bioinformatics 19, 1-9, 2018 | 164 | 2018 |
| Kohn-Sham equations as regularizer: Building prior knowledge into machine-learned physics L Li, S Hoyer, R Pederson, R Sun, ED Cubuk, P Riley, K Burke Physical review letters 126 (3), 036401, 2021 | 160 | 2021 |
| Neural reparameterization improves structural optimization S Hoyer, J Sohl-Dickstein, S Greydanus arXiv preprint arXiv:1909.04240, 2019 | 91 | 2019 |
| Machine learning guided aptamer refinement and discovery A Bashir, Q Yang, J Wang, S Hoyer, W Chou, C McLean, G Davis, Q Gong, ... Nature Communications 12 (1), 2366, 2021 | 77 | 2021 |
| Learned discretizations for passive scalar advection in a two-dimensional turbulent flow J Zhuang, D Kochkov, Y Bar-Sinai, MP Brenner, S Hoyer Physical Review Fluids 6 (6), 064605, 2021 | 63 | 2021 |
| Spatial propagation of excitonic coherence enables ratcheted energy transfer S Hoyer, A Ishizaki, KB Whaley Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 86 (4 …, 2012 | 51 | 2012 |
| Generalized master equation with non-Markovian multichromophoric Förster resonance energy transfer for modular exciton densities S Jang, S Hoyer, G Fleming, KB Whaley Physical Review Letters 113 (18), 188102, 2014 | 43 | 2014 |
| Realistic and verifiable coherent control of excitonic states in a light-harvesting complex S Hoyer, F Caruso, S Montangero, M Sarovar, T Calarco, MB Plenio, ... New Journal of Physics 16 (4), 045007, 2014 | 42 | 2014 |
| corner. py: corner. py v2. 0.0 D Foreman-Mackey, W Vousden, A Price-Whelan, M Pitkin, V Zabalza, ... Zenodo, 2016 | 40 | 2016 |
Michael P. BrennerHarvard University在 seas.harvard.edu 的电子邮件经过验证
