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We apply Physics-Informed Neural Networks (PINNs) for solving identification problems of nonhomogeneous materials.
Understanding the difficulty of training deep feedforward neural networks,
X. Glorot, Y. Bengio, · 2010
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Model-based elastography: a survey of approaches to the inverse elasticity problem,
M. M. Doyley, · 2012
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Adam: A method for stochastic optimization,
D. P. Kingma, J. Ba, · 2014
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Deep learning,
Y. LeCun, Y. Bengio, G. Hinton, · 2015
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I. Goodfellow, Y. Bengio, A. Courville, Y. Bengio, Deep learning, volume 1, MIT press Cambridge, 2016
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Applications of deep learning in biomedicine,
P. Mamoshina, A. Vieira, E. Putin, A. Zhavoronkov, · 2016
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Computational mechanics enhanced by deep learning,
A. Oishi, G. Yagawa, · 2017
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Physics-informed generative adversarial networks for stochastic differential equations,
L. Yang, D. Zhang, G. E. Karniadakis, · 2018
Cited alongside, same era.
A review of the application of machine learning and data mining approaches in continuum materials mechanics,
F. E. Bock, R. C. Aydin, C. J. Cyron, N. Huber, S. R. Kalidindi, B. Klusemann, · 2019
Cited alongside, same era.
Deep learning for molecular design—a review of the state of the art,
D. C. Elton, Z. Boukouvalas, M. D. Fuge, P. W. Chung, · 2019
Cited alongside, same era.
Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,
M. Raissi, P. Perdikaris, G. E. Karniadakis, · 2019
Cited alongside, same era.
fpinns: Fractional physics-informed neural networks,
G. Pang, L. Lu, G. E. Karniadakis, · 2019
Cited alongside, same era.
Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations,
M. Raissi, A. Yazdani, G. E. Karniadakis, · 2020
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A deep learning framework for solution and discovery in solid mechanics,
E. Haghighat, M. Raissi, A. Moure, H. Gomez, R. Juanes, · 2020
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Non-invasive inference of thrombus material properties with physics-informed neural networks,
M. Yin, X. Zheng, J. D. Humphrey, G. E. Karniadakis, · 2020
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Physics-informed neural networks for inverse problems in nano-optics and metamaterials,
Y. Chen, L. Lu, G. E. Karniadakis, L. Dal Negro, · 2020
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L. Yang, X. Meng, G. E. Karniadakis, · 2020
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Machine learning for fluid mechanics,
S. L. Brunton, B. R. Noack, P. Koumoutsakos, · 2020
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Adaptive activation functions accelerate convergence in deep and physics-informed neural networks,
A. D. Jagtap, K. Kawaguchi, G. E. Karniadakis, · 2020
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