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Transfer learning (TL) enables the transfer of knowledge gained in learning to perform one task (source) to a related but different task (target), hence addressing the expense of data acquisition and labeling, potential computational power limitations, and dataset distribution mismatches.
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Gao, Y., Mosalam, K.M.: Deep Transfer Learning for Image-Based Structural Damage Recognition. Computer-Aided Civil and Infrastructure Engineering 33(9), 748–768 (2018)
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Raissi, M., Perdikaris, P., Karniadakis, G.E.: Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. Journal of Computational Physics 378, 686–707 (2019)
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Ruder, S., Peters, M.E., Swayamdipta, S., Wolf, T.: Transfer Learning in Natural Language Processing. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Tutorials, 15–18 (2019)
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Ahmed, N., Rafiq, M., Rehman, M., Iqbal, M., Ali, M.: Numerical modeling of three dimensional Brusselator reaction diffusion system. AIP Advances 9(1), 015205 (2019)
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Niu, S., Liu, Y., Wang, J., Song, H.: A Decade Survey of Transfer Learning (2010–2020). IEEE Transactions on Artificial Intelligence 1(2), 151–166 (2020)
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Olivier, A., Shields, M.D., Graham-Brady, L.: Bayesian neural networks for uncertainty quantification in data-driven materials modeling. Computer Methods in Applied Mechanics and Engineering 386, 114079 (2021)
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Yang, X., Zhang, Y., Lv, W., Wang, D.: Image recognition of wind turbine blade damage based on a deep learning model with transfer learning and an ensemble learning classifier. Renewable Energy 163, 386–397 (2021)
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Zhang, S., Chen, M., Chen, J., Li, Y.-F., Wu, Y., Li, M., Zhu, C.: Combining cross-modal knowledge transfer and semi-supervised learning for speech emotion recognition. Knowledge-Based Systems 229, 107340 (2021)
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Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., Xiong, H., He, Q.: A Comprehensive Survey on Transfer Learning. Proceedings of the IEEE 109(1), 43–76 (2020)
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Chen, G., Li, Y., Liu, X.: Transfer Learning Under Conditional Shift Based on Fuzzy Residual. IEEE Transactions on Cybernetics (2020)
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Zhang, X., Garikipati, K.: Machine learning materials physics: Multi- resolution neural networks learn the free energy and nonlinear elastic response of evolving microstructures. Computer Methods in Applied Mechanics and Engineering 372, 113362 (2020)
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Goswami, S., Anitescu, C., Chakraborty, S., Rabczuk, T.: Transfer learning enhanced physics informed neural network for phase-field modeling of fracture. Theoretical and Applied Fracture Mechanics 106, 102447 (2020)
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Neyshabur, B., Sedghi, H., Zhang, C.: What is being transferred in transfer learning? Advances in Neural Information Processing Systems 33, 512–523 (2020)
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Yu, S., Shaker, A., Alesiani, F., Principe, J.C.: Measuring the Discrepancy between Conditional Distributions: Methods, Properties and Applications. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence, 2777–2784 (2020)
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Li, Z., Kovachki, N.B., Azizzadenesheli, K., liu, B., Bhattacharya, K., Stuart, A., Anandkumar, A.: Fourier Neural Operator for Parametric Partial Differential Equations. In: In Proceedings of the International Conference on Learning Representations (2021)
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Liu, X., Li, Y., Meng, Q., Chen, G.: Deep transfer learning for conditional shift in regression. Knowledge-Based Systems 227, 107216 (2021)
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Chen, X., Gong, C., Wan, Q., Deng, L., Wan, Y., Liu, Y., Chen, B., Liu, J.: Transfer learning for deep neural network-based partial differential equations solving. Advances in Aerodynamics 3(1), 1–14 (2021)
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2021
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Wang, H., Planas, R., Chandramowlishwaran, A., Bostanabad, R.: Mosaic flows: A transferable deep learning framework for solving PDEs on unseen domains. Computer Methods in Applied Mechanics and Engineering 389, 114424 (2022)
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Lu, L., Meng, X., Cai, S., Mao, Z., Goswami, S., Zhang, Z., Karniadakis, G.E.: A comprehensive and fair comparison of two neural operators (with practical extensions) based on FAIR data. Computer Methods in Applied Mechanics and Engineering 393, 114778 (2022)
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Kontolati, K., Goswami, S., Shields, M.D., Karniadakis, G.E., TL-DeepONet: Codes for deep transfer operator learning for partial differential equations under conditional shift. DOI: https://doi.org/10.5281/zenodo.7195684 (2022)
2022
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