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We introduce an optimized physics-informed neural network (PINN) trained to solve the problem of identifying and characterizing a surface breaking crack in a metal plate.
Near-field ultrasonic scattering from surface-breaking cracks
James L Blackshire and Shamachary Sathish · 2002
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Wave fields in real media: Wave propagation in anisotropic, anelastic, porous and electromagnetic media
José M Carcione · 2007
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Nn-align. an artificial neural network-based alignment algorithm for MHC class II peptide binding prediction
Morten Nielsen and Ole Lund · 2009
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Scikit-learn: Machine learning in Python
Fabian Pedregosa, Gaël Varoquaux, Aexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, Jake Vanderplas, Aexandre Passos, David Cournapeau, Matthieu Brucher, Matthie Perrot, and Édouard Duchesnay · 2011
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Ultrasonic scattering from complex crack morphology features
James L Blackshire · 2012
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Laser-vibrometric analysis of propagation and interaction of lamb waves in cfrp-plates
Jürgen Pohl and Gerhard Mook · 2013
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Embedded multi-tone ultrasonic excitation and continuous-scanning laser doppler vibrometry for rapid and remote imaging of structural defects
Eric B Flynn · 2014
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Lamb wave frequency–wavenumber analysis and decomposition
Zhenhua Tian and Lingyu Yu · 2014
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
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Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 2015
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Predicting the sequence specificities of DNA-and RNA-binding proteins by deep learning
Babak Alipanahi, Andrew Delong, Matthew T Weirauch, and Brendan J Frey · 2015
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Isolation of ultrasonic scattering by wavefield baseline subtraction
Alexander J Dawson, Jennifer E Michaels, and Thomas E Michaels · 2016
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Enhanced damage characterization using wavefield imaging methods
Accurate, large minibatch sgd: Training imagenet in 1 hour
Priya Goyal, Piotr Dollár, Ross Girshick, Pieter Noordhuis, Lukasz Wesolowski, Aapo Kyrola, Andrew Tulloch, Yangqing Jia, and Kaiming He · 2017
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Hidden physics models: Machine learning of nonlinear partial differential equations
Maziar Raissi and George Em Karniadakis · 2018
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Automatic differentiation in machine learning: a survey
Atilim Gunes Baydin, Barak A Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind · 2018
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Benchmarking TPU, GPU, and CPU platforms for deep learning
Gu-Yeon Wei, David Brooks, et al · 2019
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
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James L Blackshire · 2017
Cited alongside, same era.
Machine learning of linear differential equations using gaussian processes
Maziar Raissi, Paris Perdikaris, and George Em Karniadakis · 2017
Cited alongside, same era.
Data-driven discovery of partial differential equations
Samuel H Rudy, Steven L Brunton, Joshua L Proctor, and J Nathan Kutz · 2017
Cited alongside, same era.
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Deep learning of vortex-induced vibrations
Maziar Raissi, Zhicheng Wang, Michael S Triantafyllou, and George Em Karniadakis · 2019
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https://www.nde-ed.org/GeneralResources/MaterialProperties/UT/ut_matlprop_metals.htm
Acoustic properties for metals in solid: From NDT Resource Center · 2019
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Adaptive activation functions accelerate convergence in deep and physics-informed neural networks
Ameya D Jagtap, Kenji Kawaguchi, and George Em Karniadakis · 2020
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