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This paper introduces an innovative physics-informed deep learning framework for metamodeling of nonlinear structural systems with scarce data.
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R. Zhang, Z. Chen, S. Chen, J. Zheng, O. Büyüköztürk, H. Sun, Deep long short-term memory networks for nonlinear structural seismic response prediction, Computers & Structures 220 (2019) 55–68
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K. Wang, W. Sun, Meta-modeling game for deriving theory-consistent, microstructure-based traction–separation laws via deep reinforcement learning, Computer Methods in Applied Mechanics and Engineering 346 (2019) 216–241
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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, Journal of Computational Physics 378 (2019) 686–707
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X. Yang, S. Zafar, J.-X. Wang, H. Xiao, Predictive large-eddy-simulation wall modeling via physics-informed neural networks, Physical Review Fluids 4 (3) (2019) 034602
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Y. Zhu, N. Zabaras, P.-S. Koutsourelakis, P. Perdikaris, Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data, Journal of Computational Physics 394 (2019) 56–81
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G. Kissas, Y. Yang, E. Hwuang, W. R. Witschey, J. A. Detre, P. Perdikaris, Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4d flow mri data using physics-informed neural networks, Computer Methods in Applied Mechanics and Engineering 358 (2020) 112623
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