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We investigate the use of Physics-Informed Neural Networks (PINNs) for solving the wave equation.
On the Partial Difference Equations of Mathematical Physics
R. Courant, K. Friedrichs, and H. Lewy · 1967
Earlier work this paper cites.
Quantitative seismology
Keiiti Aki and Paul G Richards · 1980
Earlier work this paper cites.
Inverse problem theory: methods for data fitting and model parameter estimation
Albert Tarantola · 1987
Earlier work this paper cites.
Earthquake Hazard Analysis: Issues and insights-Springer Seismic Hazard Assessment: Issues and Alternatives
L Reiter · 1990
Earlier work this paper cites.
Introduction to the spectral element method for three-dimensional seismic wave propagation
Dimitri Komatitsch and Jeroen Tromp · 1999
Earlier work this paper cites.
Parallel 3-D viscoelastic finite difference seismic modelling
Thomas Bohlen · 2002
Earlier work this paper cites.
Simulation of ground motion using the stochastic method
David M. Boore · 2003
Earlier work this paper cites.
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Lanbo Liu and Tieshuan Guo · 2005
Earlier work this paper cites.
Marmousi2: An elastic upgrade for Marmousi
Gary S. Martin, Robert Wiley, and Kurt J. Marfurt · 2006
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
An overview of full-waveform inversion in exploration geophysics
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Earlier work this paper cites.
Scalable Earthquake Simulation on Petascale Supercomputers
Yifeng Cui, Kim B Olsen, Thomas H Jordan, Kwangyoon Lee, Jun Zhou, Patrick Small, Daniel Roten, Geoffrey Ely, Dhabaleswar K. Panda, Amit Chourasia, John Levesque, Steven M. Day, and Philip Maechling · 2010
Earlier work this paper cites.
Full Seismic Waveform Modelling and Inversion
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Earlier work this paper cites.
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Andrea Colombi, Tarje Nissen-Meyer, Lapo Boschi, and Domenico Giardini · 2012
Earlier work this paper cites.
A temporal fourth-order scheme for the first-order acoustic wave equations
Guihua Long, Yubo Zhao, and Jun Zou · 2013
Earlier work this paper cites.
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Earlier work this paper cites.
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Diederik P. Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
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T Nissen-Meyer, M. Van Driel, S C Stähler, K Hosseini, S Hempel, L Auer, A Colombi, and A Fournier · 2014
Earlier work this paper cites.
Optimized viscoelastic wave propagation for weakly dissipative media
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URL https://www.tensorflow.org
TensorFlow, 2015 · 2015
Cited alongside, same era.
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URL https://www.pytorch.org
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