Abstract
We propose a transfer learning for reservoir computing, and verify the effectivity of the proposed methods for the standard inference task of the Lorenz system. Applying the proposed methods to an inference task of fluid physics, we show the inference accuracy is drastically improved compared with the conventional reservoir computing method if available training data size is highly limited.
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Inubushi, M., Goto, S.: Inference of the energy dissipation rate of turbulence by machine learning (in preparation)
Inubushi, M., Yoshimura, K.: Reservoir computing beyond memory-nonlinearity trade-off. Sci. Rep. 7(1), 10199 (2017)
Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks-with an erratum note. Bonn Ger.: Ger. Natl. Res. Cent. Inf. Technol. GMD Tech. Rep. 148(34), 13 (2001)
Lu, Z., Pathak, J., Hunt, B., Girvan, M., Brockett, R., Ott, E.: Reservoir observers: model-free inference of unmeasured variables in chaotic systems. Chaos: Interdisc. J. Nonlinear Sci. 27(4), 041102 (2017)
Silver, D., et al.: Best of NIPS 2005: highlights on the ‘inductive transfer: 10 years later’ workshop (2006)
Weiss, K., Khoshgoftaar, T.M., Wang, D.: A survey of transfer learning. J. Big data 3(1), 9 (2016)
Wu, J.L., Xiao, H., Paterson, E.: Physics-informed machine learning approach for augmenting turbulence models: a comprehensive framework. Phys. Rev. Fluids 3(7), 074602 (2018)
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Inubushi, M., Goto, S. (2019). Transferring Reservoir Computing: Formulation and Application to Fluid Physics. In: Tetko, I., Kůrková, V., Karpov, P., Theis, F. (eds) Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. ICANN 2019. Lecture Notes in Computer Science(), vol 11731. Springer, Cham. https://doi.org/10.1007/978-3-030-30493-5_22
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DOI: https://doi.org/10.1007/978-3-030-30493-5_22
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