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We introduce the Laplace neural operator (LNO), which leverages the Laplace transform to decompose the input space.
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S.-L. J. Hu, F. Liu, B. Gao, H. Li, Pole-residue method for numerical dynamic analysis, Journal of Engineering Mechanics 142 (8) (2016) 04016045
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A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al., PyTorch: An Imperative Style, High-Performance Deep Learning Library, Advances in neural information processing systems 32 (2019)
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2020
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L. Lu, P. Jin, G. Pang, Z. Zhang, G. Karniadakis, Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators, Nature Machine Intelligence 3 (3) (2021) 218–229
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Later among the works it cites.
Y. Shi, Analysis on averaging Lorenz system and its application to climate, Ph.D. thesis, University of Minnesota (2021)
2021
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Q. Cao, S.-L. James Hu, H. Li, Laplace-and frequency-domain methods on computing transient responses of oscillators with hysteretic dampings to deterministic loading, Journal of Engineering Mechanics 149 (3) (2023) 04023005
2023
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