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1Discovering governing equations from data by sparse identification of nonlinear dynamical systems
S. Brunton, J. Proctor, and J. Kutz. Proceedings of the National Academy of Sciences, 113 (15): 3932-3937 (2016)
a year ago by @adulny
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1Physics-informed learning of governing equations from scarce data
Z. Chen, Y. Liu, and H. Sun. Nature Communications, 12 (1): 6136 (Oct 21, 2021)
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1Deep Symbolic Regression for Recurrent Sequences
S. d'Ascoli, P. Kamienny, G. Lample, and F. Charton. (2022)cite arxiv:2201.04600.
a year ago by @adulny
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2Symbolic Regression via Control Variable Genetic Programming
N. Jiang, and Y. Xue. (2023)cite arxiv:2306.08057.
a year ago by @adulny
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2End-to-end Symbolic Regression with Transformers
P. Kamienny, S. d'Ascoli, G. Lample, and F. Charton. Advances in Neural Information Processing Systems, (2022)
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2Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
S. Kim, P. Lu, S. Mukherjee, M. Gilbert, L. Jing, V. Čeperić, and M. Soljačić. IEEE Transactions on Neural Networks and Learning Systems, 32 (9): 4166-4177 (2020)
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3A Unified Framework for Deep Symbolic Regression
M. Landajuela, C. Lee, J. Yang, R. Glatt, C. Santiago, I. Aravena, T. Mundhenk, G. Mulcahy, and B. Petersen. Advances in Neural Information Processing Systems, (2022)
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3PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network
Z. Long, Y. Lu, and B. Dong. (2018)cite arxiv:1812.04426Comment: 16 pages, 15 figures. arXiv admin note: substantial text overlap with arXiv:1710.09668.
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1Efficient Generator of Mathematical Expressions for Symbolic Regression
S. Mežnar, S. Džeroski, and L. Todorovski. (2023)cite arxiv:2302.09893Comment: 35 pages, 11 tables, 7 multi-part figures, Machine learning (Springer) and journal track of ECML/PKDD 2023.
a year ago by @adulny
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1Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients
B. Petersen, M. Larma, T. Mundhenk, C. Santiago, S. Kim, and J. Kim. International Conference on Learning Representations, (2021)
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4SymbolicGPT: A Generative Transformer Model for Symbolic Regression
M. Valipour, B. You, M. Panju, and A. Ghodsi. (2021)cite arxiv:2106.14131Comment: 11 pages, 4 figures.
a year ago by @adulny
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3Symbolic Brittleness in Sequence Models: On Systematic Generalization in Symbolic Mathematics
S. Welleck, P. West, J. Cao, and Y. Choi. Proceedings of the AAAI Conference on Artificial Intelligence, 36 (8): 8629-8637 (June 2022)
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2Discovery of Partial Differential Equations from Highly Noisy and Sparse Data with Physics-Informed Information Criterion
H. Xu, J. Zeng, and D. Zhang. Research, (2023)
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2Robust discovery of partial differential equations in complex situations
H. Xu, and D. Zhang. Phys. Rev. Res., 3 (3): 033270 (September 2021)
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1Discovering governing equations from data by sparse identification of nonlinear dynamical systems

1Physics-informed learning of governing equations from scarce data

1Deep Symbolic Regression for Recurrent Sequences

2Symbolic Regression via Control Variable Genetic Programming

2End-to-end Symbolic Regression with Transformers

2Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery

3A Unified Framework for Deep Symbolic Regression

3PDE-Net 2.0: Learning PDEs from Data with A Numeric-Symbolic Hybrid Deep Network

1Efficient Generator of Mathematical Expressions for Symbolic Regression

1Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients

4SymbolicGPT: A Generative Transformer Model for Symbolic Regression

3Symbolic Brittleness in Sequence Models: On Systematic Generalization in Symbolic Mathematics

2Discovery of Partial Differential Equations from Highly Noisy and Sparse Data with Physics-Informed Information Criterion

2Robust discovery of partial differential equations in complex situations

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