Machine learned Green's functions that approximately satisfy the wave equation
暂无分享,去创建一个
[1] Michal Malinowski,et al. Automatic 3D illumination-diagnosis method for large-N arrays: robust data scanner and machine-learning feature provider , 2019 .
[2] Mauricio Araya-Polo,et al. Deep learning-driven velocity model building workflow , 2019, The Leading Edge.
[3] Paris Perdikaris,et al. Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations , 2019, J. Comput. Phys..
[4] T. Hughes. Multiscale phenomena: Green's functions, the Dirichlet-to-Neumann formulation, subgrid scale models, bubbles and the origins of stabilized methods , 1995 .
[5] Ghassan AlRegib,et al. A comparison of seismic saltbody interpretation via neural networks at sample and pattern levels , 2019, Geophysical Prospecting.
[6] R. Pratt. Seismic waveform inversion in the frequency domain; Part 1, Theory and verification in a physical scale model , 1999 .
[7] Jorge Nocedal,et al. On the limited memory BFGS method for large scale optimization , 1989, Math. Program..
[8] Indranil Pan,et al. Seismic facies analysis using machine learning , 2018, Geophysics.
[9] Kevin Stanley McFall,et al. Artificial Neural Network Method for Solution of Boundary Value Problems With Exact Satisfaction of Arbitrary Boundary Conditions , 2009, IEEE Transactions on Neural Networks.