Peter Y. Lu

20 papers A* 3Journal 17
YearRankTypeTitle / Venue / Authors
2025 J jnl
Hierarchical Implicit Neural Emulators.
CoRR
Ruoxi Jiang, Xiao Zhang, Karan Jakhar, Peter Y. Lu, Pedram Hassanzadeh, Michael Maire, Rebecca Willett
2025 J jnl
The Future of Artificial Intelligence and the Mathematical and Physical Sciences (AI+MPS).
CoRR
Andrew Ferguson, Marisa Lafleur, Lars Ruthotto, Jesse Thaler, Yuan-Sen Ting, Pratyush Tiwary, Soledad Villar, E. Paulo Alves, Jeremy Avigad, Simon Billinge, Camille L. Bilodeau, Keith Brown, Emmanuel J. Candès, Arghya Chattopadhyay, Bingqing Cheng, Jonathan Clausen, Connor W. Coley, Andrew J. Connolly, Fred Daum, Sijia S. Dong, Chrisy Xiyu Du, Cora Dvorkin, Cristiano Fanelli, Eric B. Ford, Luis Manuel Frutos, Nicolás García Trillos, Cecilia Garraffo, Robert Ghrist, Rafael Gómez-Bombarelli, Gianluca Guadagni, Sreelekha Guggilam, Sergei Gukov, Juan B. Gutierrez, Salman Habib, Johannes Hachmann, Boris Hanin, Philip C. Harris, Murray Holland, Elizabeth Holm, Hsin-Yuan Huang, Shih-Chieh Hsu, Nick Jackson, Olexandr Isayev, Heng Ji, Aggelos K. Katsaggelos, Jeremy Kepner, Yannis G. Kevrekidis, Michelle P. Kuchera, J. Nathan Kutz, Branislava Lalic, Ann Lee, Matt LeBlanc, Josiah Lim, Rebecca Lindsey, Yongmin Liu, Peter Y. Lu, Sudhir Malik, Vuk Mandic, Vidya B. Manian, Emeka P. Mazi, Pankaj Mehta, Peter Melchior, Brice Ménard, Jennifer Ngadiuba, Stella Offner, Elsa Olivetti, Shyue Ping Ong, Christopher Rackauckas, Philippe Rigollet, Chad Risko, Philip Romero, Grant M. Rotskoff, Brett Savoie, Uros Seljak, David Shih, Gary Shiu, Dima Shlyakhtenko, Eva Silverstein, Taylor Sparks, Thomas Strohmer, Christopher Stubbs, Stephen Thomas, Suriyanarayanan Vaikuntanathan, René Vidal, Francisco Villaescusa-Navarro, Gregory Voth, Benjamin Wandelt, Rachel Ward, Melanie Weber, Risa Wechsler, Stephen Whitelam, Olaf Wiest, Mike Williams, Zhuoran Yang, Yaroslava G. Yingling, Bin Yu, Shuwen Yue, Ann Zabludoff, Huimin Zhao, Tong Zhang
2024 J jnl
Deep Learning and Symbolic Regression for Discovering Parametric Equations.
IEEE Trans. Neural Networks Learn. Syst.
Michael Zhang, Samuel Kim, Peter Y. Lu, Marin Soljacic
2024 A* conf
Deep Stochastic Mechanics.
ICML
Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang, Peter Y. Lu, Rebecca Willett
2024 J jnl
Embed and Emulate: Contrastive representations for simulation-based inference.
CoRR
Ruoxi Jiang, Peter Y. Lu, Rebecca Willett
2023 J jnl
Deep Stochastic Mechanics.
CoRR
Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang, Peter Y. Lu, Rebecca Willett
2023 J jnl
Model Stitching: Looking For Functional Similarity Between Representations.
CoRR
Adriano Hernandez, Rumen Dangovski, Peter Y. Lu, Marin Soljacic
2023 J jnl
Multimodal Learning for Crystalline Materials.
CoRR
Viggo Moro, Charlotte Loh, Rumen Dangovski, Ali Ghorashi, Andrew Ma, Zhuo Chen, Peter Y. Lu, Thomas Christensen, Marin Soljacic
2023 A* conf
Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows.
ICML
Owen M. Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljacic
2023 J jnl
Q-Flow: Generative Modeling for Differential Equations of Open Quantum Dynamics with Normalizing Flows.
CoRR
Owen Dugan, Peter Y. Lu, Rumen Dangovski, Di Luo, Marin Soljacic
2023 A* conf
Training neural operators to preserve invariant measures of chaotic attractors.
NeurIPS
Ruoxi Jiang, Peter Y. Lu, Elena Orlova, Rebecca Willett
2023 J jnl
Training neural operators to preserve invariant measures of chaotic attractors.
CoRR
Ruoxi Jiang, Peter Y. Lu, Elena Orlova, Rebecca Willett
2022 J jnl
Deep Learning and Symbolic Regression for Discovering Parametric Equations.
CoRR
Michael Zhang, Samuel Kim, Peter Y. Lu, Marin Soljacic
2022 J jnl
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure.
Trans. Mach. Learn. Res.
Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic
2022 J jnl
Discovering Conservation Laws using Optimal Transport and Manifold Learning.
CoRR
Peter Y. Lu, Rumen Dangovski, Marin Soljacic
2021 J jnl
Discovering Sparse Interpretable Dynamics from Partial Observations.
CoRR
Peter Y. Lu, Joan Ariño, Marin Soljacic
2021 J jnl
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery.
IEEE Trans. Neural Networks Learn. Syst.
Samuel Kim, Peter Y. Lu, Srijon Mukherjee, Michael Gilbert, Li Jing, Vladimir Ceperic, Marin Soljacic
2021 J jnl
Scalable and Flexible Deep Bayesian Optimization with Auxiliary Information for Scientific Problems.
CoRR
Samuel Kim, Peter Y. Lu, Charlotte Loh, Jamie Smith, Jasper Snoek, Marin Soljacic
2019 J jnl
Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning.
CoRR
Peter Y. Lu, Samuel Kim, Marin Soljacic
2019 J jnl
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery.
CoRR
Samuel Kim, Peter Y. Lu, Srijon Mukherjee, Michael Gilbert, Li Jing, Vladimir Ceperic, Marin Soljacic