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Learned graph neural networks (GNNs) have recently been established as fast and accurate alternatives for principled solvers in simulating the dynamics of physical systems.
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Parametric convolutional neural network-domain full-waveform inversion
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Deepsdf: Learning continuous signed distance functions for shape representation
Park, J. J., Florence, P., Straub, J., Newcombe, R., and Lovegrove, S · 2019
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Fourier features let networks learn high frequency functions in low dimensional domains
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Machine learning–accelerated computational fluid dynamics
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Fourier neural operator for parametric partial differential equations
Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., and Anandkumar, A · 2021
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Learning nonlinear operators via deeponet based on the universal approximation theorem of operators
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Pivotal tuning for latent-based editing of real images
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Learning incompressible fluid dynamics from scratch–towards fast, differentiable fluid models that generalize
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Long-time integration of parametric evolution equations with physics-informed deeponets
Wang, S. and Perdikaris, P · 2021
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Learning the solution operator of parametric partial differential equations with physics-informed deeponets
Wang, S., Wang, H., and Perdikaris, P · 2021
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Seismic Wave Propagation and Inversion with Neural Operators
Yang, Y., Gao, A. F., Castellanos, J. C., Ross, Z. E., Azizzadenesheli, K., and Clayton, R. W · 2021
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Learning mesh-based simulation with graph networks
Pfaff, T., Fortunato, M., Sanchez-Gonzalez, A., and Battaglia, P. W · 2021
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