Physics-informed deep learning for flow and deformation in poroelastic media

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E. Zhang, M. Yin, G. E. Karniadakis, Physics-Informed Neural Networks for Nonhomogeneous Material Identification in Elasticity Imaging, arXiv preprint arXiv:2009.04525

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H. Guo, X. Zhuang, T. Rabczuk, A deep collocation method for the bending analysis of Kirchhoff plate, Comput Mater Continua 59 (2) (2019) 433–456

2019

Later among the works it cites.

Y. Yang, P. Perdikaris, Adversarial uncertainty quantification in physics-informed neural networks, Journal of Computational Physics 394 (2019) 136–152

2019

Later among the works it cites.

Z. Mao, A. D. Jagtap, G. E. Karniadakis, Physics-informed neural networks for high-speed flows, Computer Methods in Applied Mechanics and Engineering 360 (2020) 112789

2020

Closest in time.

X. Meng, G. E. Karniadakis, A composite neural network that learns from multi-fidelity data: Application to function approximation and inverse PDE problems, Journal of Computational Physics 401 (2020) 109020

2020

Closest in time.