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We propose a new composite neural network (NN) that can be trained based on multi-fidelity data.
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N. V. Nguyen, S. M. Choi, W. S. Kim, J. W. Lee, S. Kim, D. Neufeld, Y. H. Byun, Multidisciplinary unmanned combat air vehicle system design using multi-fidelity model, Aerosp. Sci. and Technol. 26 (1) (2013) 200–210
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N. Dhir, A. R. Kosiorek, I. Posner, Bayesian delay embeddings for dynamical systems, Conference on Neural Information Processing Systems, 2017
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L. Bonfiglio, P. Perdikaris, G. Vernengo, J. S. de Medeiros, G. E. Karniadakis, Improving swath seakeeping performance using multi-fidelity Gaussian process and Bayesian optimization, J. Ship Res. 62 (4) (2018) 223–240
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M. Raissi, P. Perdikaris, G. E. Karniadakis, Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations, J. Comput. Phys. 378 (2019) 686–707
2019
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