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Numerical simulations play a critical role in design and development of engineering products and processes.
Finite volume methods for hyperbolic problems , volume 31
LeVeque, R. J · 2002
Earlier work this paper cites.
Openfoam: A c++ library for complex physics simulations
Jasak, H., Jemcov, A., Tukovic, Z., et al · 2007
Earlier work this paper cites.
Finite difference methods for ordinary and partial differential equations: steady-state and time-dependent problems
LeVeque, R. J · 2007
Earlier work this paper cites.
Fourier neural operator for parametric partial differential equations
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Earlier work this paper cites.
Gated graph sequence neural networks
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Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation
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Earlier work this paper cites.
Lu, L., Jin, P., and Karniadakis, G. E · 2019
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Learning mesh-based simulation with graph networks
Pfaff, T., Fortunato, M., Sanchez-Gonzalez, A., and Battaglia, P. W · 2020
Earlier work this paper cites.
Learning to simulate complex physics with graph networks
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Earlier work this paper cites.
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Sampling-based distributed training with message passing neural network
Kakka, P., Nidhan, S., Ranade, R., and MacArt, J. F · 2024
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Geometry-informed neural operator for large-scale 3d pdes
Li, Z., Kovachki, N., Choy, C., Li, B., Kossaifi, J., Otta, S., Nabian, M. A., Stadler, M., Hundt, C., Azizzadenesheli, K., et al · 2024
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