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Peter Y. Lu
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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
1592020
Extracting Interpretable Physical Parameters from Spatiotemporal Systems Using Unsupervised Learning
PY Lu, S Kim, M Soljačić
Physical Review X 10 (3), 031056, 2020
652020
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
252013
Discovering sparse interpretable dynamics from partial observations
PY Lu, J Ariño Bernad, M Soljačić
Communications Physics 5 (1), 206, 2022
222022
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
202013
Discovering conservation laws using optimal transport and manifold learning
PY Lu, R Dangovski, M Soljačić
Nature Communications 14 (1), 4744, 2023
122023
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
122016
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
92023
Discovering dynamical parameters by interpreting echo state networks
O Alao, PY Lu, M Soljacic
NeurIPS 2021 AI for Science Workshop, 2021
62021
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
52023
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
32024
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
12023
Model stitching: Looking for functional similarity between representations
A Hernandez, R Dangovski, PY Lu, M Soljacic
arXiv preprint arXiv:2303.11277, 2023
12023
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
12023
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
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