| Learning partial differential equations via data discovery and sparse optimization H Schaeffer Proceedings of the Royal Society A: Mathematical, Physical and Engineering …, 2017 | 543 | 2017 |
| Sparse model selection via integral terms H Schaeffer, SG McCalla Physical Review E 96 (2), 023302, 2017 | 224 | 2017 |
| Sparse dynamics for partial differential equations H Schaeffer, R Caflisch, CD Hauck, S Osher Proceedings of the National Academy of Sciences 110 (17), 6634-6639, 2013 | 213 | 2013 |
| On the convergence of the SINDy algorithm L Zhang, H Schaeffer Multiscale Modeling & Simulation 17 (3), 948-972, 2019 | 208 | 2019 |
| Extracting sparse high-dimensional dynamics from limited data H Schaeffer, G Tran, R Ward SIAM Journal on Applied Mathematics 78 (6), 3279-3295, 2018 | 191 | 2018 |
| A low patch-rank interpretation of texture H Schaeffer, S Osher SIAM Journal on Imaging Sciences 6 (1), 226-262, 2013 | 157 | 2013 |
| Neupde: Neural network based ordinary and partial differential equations for modeling time-dependent data Y Sun, L Zhang, H Schaeffer Mathematical and Scientific Machine Learning, 352-372, 2020 | 112 | 2020 |
| Extracting structured dynamical systems using sparse optimization with very few samples H Schaeffer, G Tran, R Ward, L Zhang Multiscale Modeling & Simulation 18 (4), 1435-1461, 2020 | 64 | 2020 |
| Forward stability of ResNet and its variants L Zhang, H Schaeffer Journal of Mathematical Imaging and Vision 62, 328-351, 2020 | 63 | 2020 |
| Generalization bounds for sparse random feature expansions A Hashemi, H Schaeffer, R Shi, U Topcu, G Tran, R Ward Applied and Computational Harmonic Analysis 62, 310-330, 2023 | 60 | 2023 |
| On the compressive spectral method A Mackey, H Schaeffer, S Osher Multiscale Modeling & Simulation 12 (4), 1800-1827, 2014 | 60 | 2014 |
| An Penalty Method for General Obstacle Problems G Tran, H Schaeffer, WM Feldman, SJ Osher SIAM Journal on Applied Mathematics 75 (4), 1424-1444, 2015 | 43 | 2015 |
| Learning dynamical systems and bifurcation via group sparsity H Schaeffer, G Tran, R Ward arXiv preprint arXiv:1709.01558, 2017 | 34 | 2017 |
| BelNet: basis enhanced learning, a mesh-free neural operator Z Zhang, L Wing Tat, H Schaeffer Proceedings of the Royal Society A 479 (2276), 20230043, 2023 | 32 | 2023 |
| PDEs with compressed solutions RE Caflisch, SJ Osher, H Schaeffer, G Tran arXiv preprint arXiv:1311.5850, 2013 | 26 | 2013 |
| Conditioning of random Fourier feature matrices: double descent and generalization error Z Chen, H Schaeffer Information and Inference: A Journal of the IMA 13 (2), iaad054, 2024 | 21* | 2024 |
| Recovery guarantees for polynomial coefficients from weakly dependent data with outliers LST Ho, H Schaeffer, G Tran, R Ward Journal of Approximation Theory 259, 105472, 2020 | 19* | 2020 |
| Shrimp: Sparser random feature models via iterative magnitude pruning Y Xie, R Shi, H Schaeffer, R Ward Mathematical and Scientific Machine Learning, 303-318, 2022 | 17 | 2022 |
| PROSE: Predicting Multiple Operators and Symbolic Expressions using multimodal transformers Y Liu, Z Zhang, H Schaeffer Neural Networks 180, 106707, 2024 | 16 | 2024 |
| Towards a Foundation Model for Partial Differential Equations: Multi-Operator Learning and Extrapolation J Sun, Y Liu, Z Zhang, H Schaeffer arXiv preprint arXiv:2404.12355, 2024 | 16 | 2024 |
