Peter Y. Lu

According to our database, Peter Y. Lu authored at least 13 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Deep Learning and Symbolic Regression for Discovering Parametric Equations.

Michael Zhang, Samuel Kim, Peter Y. Lu, Marin Soljacic

IEEE Trans. Neural Networks Learn. Syst., November, 2024

Embed and Emulate: Contrastive representations for simulation-based inference.

Ruoxi Jiang, Peter Y. Lu, Rebecca Willett

CoRR, 2024

Deep Stochastic Mechanics.

Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang, Peter Y. Lu, Rebecca Willett

Proceedings of the Forty-first International Conference on Machine Learning, 2024

2023

Multimodal Learning for Crystalline Materials.

Viggo Moro, Charlotte Loh, Rumen Dangovski, Ali Ghorashi, Andrew Ma, Zhuo Chen, Peter Y. Lu, Thomas Christensen, Marin Soljacic

CoRR, 2023

Model Stitching: Looking For Functional Similarity Between Representations.

Adriano Hernandez, Rumen Dangovski, Peter Y. Lu, Marin Soljacic

CoRR, 2023

Training neural operators to preserve invariant measures of chaotic attractors.

Ruoxi Jiang, Peter Y. Lu, Elena Orlova, Rebecca Willett

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows.

Owen M. Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljacic

Proceedings of the International Conference on Machine Learning, 2023

2022

Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure.

Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic

Trans. Mach. Learn. Res., 2022

Discovering Conservation Laws using Optimal Transport and Manifold Learning.

Peter Y. Lu, Rumen Dangovski, Marin Soljacic

CoRR, 2022

2021

Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery.

Samuel Kim, Peter Y. Lu, Srijon Mukherjee, Michael Gilbert, Li Jing, Vladimir Ceperic, Marin Soljacic

IEEE Trans. Neural Networks Learn. Syst., 2021

Discovering Sparse Interpretable Dynamics from Partial Observations.

Peter Y. Lu, Joan Ariño, Marin Soljacic

CoRR, 2021

Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems.

Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic

CoRR, 2021

2019

Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning.

Peter Y. Lu, Samuel Kim, Marin Soljacic

CoRR, 2019