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2020 – today
- 2024
[j3]Michael Zhang, Samuel Kim
, Peter Y. Lu, Marin Soljacic:
Deep Learning and Symbolic Regression for Discovering Parametric Equations. IEEE Trans. Neural Networks Learn. Syst. 35(11): 16775-16787 (2024)- [c3]Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang, Peter Y. Lu, Rebecca Willett:
Deep Stochastic Mechanics. ICML 2024 - [i12]Ruoxi Jiang, Peter Y. Lu, Rebecca Willett:
Embed and Emulate: Contrastive representations for simulation-based inference. CoRR abs/2409.18402 (2024) - 2023
- [c2]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: 8879-8901 - [c1]Ruoxi Jiang, Peter Y. Lu, Elena Orlova, Rebecca Willett:
Training neural operators to preserve invariant measures of chaotic attractors. NeurIPS 2023 - [i11]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 abs/2302.12235 (2023) - [i10]Adriano Hernandez, Rumen Dangovski, Peter Y. Lu, Marin Soljacic:
Model Stitching: Looking For Functional Similarity Between Representations. CoRR abs/2303.11277 (2023) - [i9]Elena Orlova, Aleksei Ustimenko, Ruoxi Jiang, Peter Y. Lu, Rebecca Willett:
Deep Stochastic Mechanics. CoRR abs/2305.19685 (2023) - [i8]Ruoxi Jiang, Peter Y. Lu, Elena Orlova, Rebecca Willett:
Training neural operators to preserve invariant measures of chaotic attractors. CoRR abs/2306.01187 (2023) - [i7]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 abs/2312.00111 (2023) - 2022
- [j2]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) - [i6]Michael Zhang, Samuel Kim, Peter Y. Lu, Marin Soljacic:
Deep Learning and Symbolic Regression for Discovering Parametric Equations. CoRR abs/2207.00529 (2022) - [i5]Peter Y. Lu, Rumen Dangovski, Marin Soljacic:
Discovering Conservation Laws using Optimal Transport and Manifold Learning. CoRR abs/2208.14995 (2022) - 2021
- [j1]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): 4166-4177 (2021) - [i4]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 abs/2104.11667 (2021) - [i3]Peter Y. Lu, Joan Ariño, Marin Soljacic:
Discovering Sparse Interpretable Dynamics from Partial Observations. CoRR abs/2107.10879 (2021)
2010 – 2019
- 2019
- [i2]Peter Y. Lu, Samuel Kim, Marin Soljacic:
Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning. CoRR abs/1907.06011 (2019) - [i1]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 abs/1912.04825 (2019)
Coauthor Index
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