Peter Y. Lu

Publication Activity (10 Years)

Years Active: 2019-2023
Publications (10 Years): 15

Top Topics

Symbolic Regression

Differential Equations

Unsupervised Learning

Top Venues

IEEE Trans. Neural Networks Learn. Syst.

Trans. Mach. Learn. Res.

Publications

Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang, Peter Y. Lu, Rebecca Willett
Deep Stochastic Mechanics. CoRR (2023)
Ruoxi Jiang, Peter Y. Lu, Elena Orlova, Rebecca Willett
Training neural operators to preserve invariant measures of chaotic attractors. CoRR (2023)
Viggo Moro, Charlotte Loh, Rumen Dangovski, Ali Ghorashi, Andrew Ma, Zhuo Chen, Peter Y. Lu, Thomas Christensen, Marin Soljacic
Multimodal Learning for Crystalline Materials. CoRR (2023)
Adriano Hernandez, Rumen Dangovski, Peter Y. Lu, Marin Soljacic
Model Stitching: Looking For Functional Similarity Between Representations. CoRR (2023)
Owen Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljacic
Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows. CoRR (2023)
Owen M. Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljacic
Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows. ICML (2023)
Ruoxi Jiang, Peter Y. Lu, Elena Orlova, Rebecca Willett
Training neural operators to preserve invariant measures of chaotic attractors. NeurIPS (2023)
Michael Zhang, Samuel Kim, Peter Y. Lu, Marin Soljacic
Deep Learning and Symbolic Regression for Discovering Parametric Equations. CoRR (2022)
Peter Y. Lu, Rumen Dangovski, Marin Soljacic
Discovering Conservation Laws using Optimal Transport and Manifold Learning. CoRR (2022)
Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure. Trans. Mach. Learn. Res. 2022 (2022)
Samuel Kim, Peter Y. Lu, Srijon Mukherjee, Michael Gilbert, Li Jing, Vladimir Ceperic, Marin Soljacic
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery. IEEE Trans. Neural Networks Learn. Syst. 32 (9) (2021)
Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic
Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems. CoRR (2021)
Peter Y. Lu, Joan Ariño, Marin Soljacic
Discovering Sparse Interpretable Dynamics from Partial Observations. CoRR (2021)
Samuel Kim, Peter Y. Lu, Srijon Mukherjee, Michael Gilbert, Li Jing, Vladimir Ceperic, Marin Soljacic
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery. CoRR (2019)
Peter Y. Lu, Samuel Kim, Marin Soljacic
Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning. CoRR (2019)