A physics-informed neural network for quantifying the microstructure properties of polycrystalline Nickel using ultrasound data

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Y. Xu, T. Aizawa and J. Kihara, Simultaneous determination of elastic constants and crystallographic orientation in coarse-grained nickel by acoustic spectro-microscopy , Materials transactions, JIM, 38, no. 6, p.536 (1997)

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J. Blackshire, and S. Sathish, Near-field ultrasonic scattering from surface-breaking cracks. , Applied Physics Letters, 80, no. 18, p.3442 (2002)

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H. Seiner, L. Bodnarova, P. Sedlak, M. Janecek, O. Srba, R. Kral, and M.Landa, Application of ultrasonic methods to determine elastic anisotropyof polycrystalline copper processed by equal-channel angular pressing , Acta Mater, 58(1), 235–247 (2010)

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J. Carcione, Wave fields in real media: Wave propagation in anisotropic, anelastic, porous and electromagnetic media , Elsevier (2014)

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M. Groeber and M. Jackson, SDREAM.3D: A digital representation environment for the analysis of microstructure in 3D , Integrating materials and manufacturing innovation, 3, no. 1, p.56 (2014)

2014

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C. Kube, and J. Turner, Ultrasonic attenuation in polycrystals using a self-consistent approach , Wave Motion, 57, p.182 (2015)

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A. Baydin, B. Pearlmutter, A. Radul, and J. Siskind, Automatic differentiation in machine learning: A survey The Journal of Machine Learning Research, 18(1), pp.5595-5637 (2017)

2017

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M. Raissi, P. Perdikaris, and G 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

2019

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K. Shukla, P. Leoni, J. Blackshire, D. Sparkman and G. Karniadakis Physics-Informed Neural Network for Ultrasound Nondestructive Quantification of Surface Breaking Cracks , J Nondestruct Eval 39, 61 (2020)

2020

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A. D. Jagtap, K. Kawaguchi, and G. E. Karniadakis, Adaptive activation functions accelerate convergence in deep and physics-informed neural networks , Journal of Computational Physics, 404, 109136 (2020)

2020

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A. D. Jagtap, K. Kawaguchi, and G. E. Karniadakis, Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks , Proceedings of the Royal Society A 476 (2239), 20200334, 2020

2020

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S. Wang, H. Wang and P. Perdikaris, On the eigenvector bias of Fourier feature networks: From regression to solving multi-scale PDEs with physics-informed neural networks , arXiv preprint, arXiv: 2012.10047 (2020)

2020

Later among the works it cites.