| Integration of neural network-based symbolic regression in deep learning for scientific discovery S Kim, PY Lu, S Mukherjee, M Gilbert, L Jing, V Čeperić, M Soljačić IEEE transactions on neural networks and learning systems 32 (9), 4166-4177, 2020 | 159 | 2020 |
| Extracting Interpretable Physical Parameters from Spatiotemporal Systems Using Unsupervised Learning PY Lu, S Kim, M Soljačić Physical Review X 10 (3), 031056, 2020 | 65 | 2020 |
| Deep learning for Bayesian optimization of scientific problems with high-dimensional structure S Kim, PY Lu, C Loh, J Smith, J Snoek, M Soljačić arXiv preprint arXiv:2104.11667, 2021 | 33* | 2021 |
| Energy loss at propagating jamming fronts in granular gas clusters JC Burton, PY Lu, SR Nagel Physical Review Letters 111 (18), 188001, 2013 | 25 | 2013 |
| Discovering sparse interpretable dynamics from partial observations PY Lu, J Ariño Bernad, M Soljačić Communications Physics 5 (1), 206, 2022 | 22 | 2022 |
| Collision dynamics of particle clusters in a two-dimensional granular gas JC Burton, PY Lu, SR Nagel Physical Review E—Statistical, Nonlinear, and Soft Matter Physics 88 (6 …, 2013 | 20 | 2013 |
| Discovering conservation laws using optimal transport and manifold learning PY Lu, R Dangovski, M Soljačić Nature Communications 14 (1), 4744, 2023 | 12 | 2023 |
| Extraordinary optical transmission inside a waveguide: spatial mode dependence KS Reichel, PY Lu, S Backus, R Mendis, DM Mittleman Optics Express 24 (25), 28221-28227, 2016 | 12 | 2016 |
| Deep learning and symbolic regression for discovering parametric equations M Zhang, S Kim, PY Lu, M Soljačić IEEE Transactions on Neural Networks and Learning Systems, 2023 | 9 | 2023 |
| Discovering dynamical parameters by interpreting echo state networks O Alao, PY Lu, M Soljacic NeurIPS 2021 AI for Science Workshop, 2021 | 6 | 2021 |
| Q-flow: generative modeling for differential equations of open quantum dynamics with normalizing flows OM Dugan, PY Lu, R Dangovski, D Luo, M Soljacic International Conference on Machine Learning, 8879-8901, 2023 | 5 | 2023 |
| Training neural operators to preserve invariant measures of chaotic attractors R Jiang, PY Lu, E Orlova, R Willett Advances in Neural Information Processing Systems 36, 2024 | 3 | 2024 |
| Multimodal learning for crystalline materials V Moro, C Loh, R Dangovski, A Ghorashi, A Ma, Z Chen, PY Lu, ... arXiv preprint arXiv:2312.00111, 2023 | 1 | 2023 |
| Model stitching: Looking for functional similarity between representations A Hernandez, R Dangovski, PY Lu, M Soljacic arXiv preprint arXiv:2303.11277, 2023 | 1 | 2023 |
| Studying Phase Transitions in Contrastive Learning With Physics-Inspired Datasets A Cy, A Chemparathy, M Han, R Dangovski, PY Lu, M Soljacic ICLR 2023 Workshop on Physics for Machine Learning, 2023 | 1 | 2023 |
| Training Machine Learning Emulators to Preserve Invariant Measures of Chaotic Attractors P Lu, R Jiang, E Orlova, R Willett Bulletin of the American Physical Society, 2024 | | 2024 |
| Q-Flow: Generative Modeling for Open Quantum Dynamics with Normalizing Flows O Dugan, P Lu, R Dangovski, D Luo, M Soljacic Bulletin of the American Physical Society, 2024 | | 2024 |
| Deep Stochastic Mechanics E Orlova, A Ustimenko, R Jiang, PY Lu, R Willett arXiv preprint arXiv:2305.19685, 2023 | | 2023 |
| NBA2Vec: Dense feature representations of NBA players W Guan, N Javed, P Lu arXiv preprint arXiv:2302.13386, 2023 | | 2023 |
| Interpretable Physics-informed Machine Learning Methods for Scientific Modeling and Data Analysis PY Lu Massachusetts Institute of Technology, 2022 | | 2022 |
Marin SoljacicProfessor of Physics, MIT在 mit.edu 的电子邮件经过验证
