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Fourier neural operators (FNOs) are a recently introduced neural network architecture for learning solution operators of partial differential equations (PDEs), which have been shown to perform significantly better than comparable deep learning approaches.
Language models are few-shot learners
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J.D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al., 2020 · 1901
Earlier work this paper cites.
Megatron-lm: Training multi-billion parameter language models using model parallelism
Shoeybi, M., Patwary, M., Puri, R., LeGresley, P., Casper, J., Catanzaro, B., 2019 · 1909
Earlier work this paper cites.
Lu, L., Jin, P., Karniadakis, G.E., 2019 · 1910
Earlier work this paper cites.
Paramesh: A parallel adaptive mesh refinement community toolkit
MacNeice, P., Olson, K.M., Mobarry, C., De Fainchtein, R., Packer, C., 2000 · 2000
Earlier work this paper cites.
Neural operator: Graph kernel network for partial differential equations
Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., Anandkumar, A., 2020b · 2003
Earlier work this paper cites.
A linear algebraic approach to model parallelism in deep learning
Hewett, R.J., Grady II, T.J., 2020 · 2006
Earlier work this paper cites.
Finite difference methods for ordinary and partial differential equations: steady-state and time-dependent problems
LeVeque, R.J., 2007 · 2007
Earlier work this paper cites.
Carbon capture and storage
Gibbins, J., Chalmers, H., 2008 · 2008
Earlier work this paper cites.
Toward adjoinable mpi, in: 2009 IEEE International Symposium on Parallel & Distributed Processing, IEEE. pp. 1–8
Utke, J., Hascoet, L., Heimbach, P., Hill, C., Hovland, P., Naumann, U., 2009 · 2009
Earlier work this paper cites.
Optimization of multilateral well design and location in a real field using a continuous genetic algorithm, in: SPE/DGS Saudi Arabia Section Technical Symposium and Exhibition, OnePetro
Bukhamsin, A.Y., Farshi, M.M., Aziz, K., 2010 · 2010
Earlier work this paper cites.
Fourier neural operator for parametric partial differential equations
Li, Z., Kovachki, N., Azizzadenesheli, K., Liu, B., Bhattacharya, K., Stuart, A., Anandkumar, A., 2020a · 2010
Earlier work this paper cites.
Application of a particle swarm optimization algorithm for determining optimum well location and type
Onwunalu, J.E., Durlofsky, L.J., 2010 · 2010
Earlier work this paper cites.
The finite element method: linear static and dynamic finite element analysis
Hughes, T.J., 2012 · 2012
Earlier work this paper cites.
Well placement optimization: A survey with special focus on application for gas/gas-condensate reservoirs
Nasrabadi, H., Morales, A., Zhu, D., 2012 · 2012
Earlier work this paper cites.
Pfft: An extension of fftw to massively parallel architectures
Pippig, M., 2013 · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Kingma, D.P., Ba, J., 2014 · 2014
Earlier work this paper cites.
The sleipner co2 storage site: using a basin model to understand reservoir simulations of plume dynamics
Andrew, J.C., Haszeldine, R.S., Nazarian, B., 2015 · 2015
Earlier work this paper cites.
Numerical analysis
Burden, R.L., Faires, J.D., Burden, A.M., 2015 · 2015
Cited alongside, same era.
{ \{ TensorFlow } \} : A system for { \{ Large-Scale } \} machine learning, in: 12th USENIX symposium on operating systems design and implementation (OSDI 16), pp. 265–283
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al., 2016 · 2016
Cited alongside, same era.
Full-waveform inversion on heterogeneous hpc systems
Gokhberg, A., Fichtner, A., 2016 · 2016
Cited alongside, same era.
20 years of monitoring co2-injection at sleipner
Furre, A.K., Eiken, O., Alnes, H., Vevatne, J.N., Kiær, A.F., 2017 · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Paszke, A., Gross, S., Chintala, S., Chanan, G., Yang, E., DeVito, Z., Lin, Z., Desmaison, A., Antiga, L., Lerer, A., 2017 · 2017
Cited alongside, same era.
Compiling machine learning programs via high-level tracing
Physics-informed machine learning
Karniadakis, G.E., Kevrekidis, I.G., Lu, L., Perdikaris, P., Wang, S., Yang, L., 2021 · 2021
Later among the works it cites.
zarr-developers/zarr-python: v2.10.3
Miles, A., jakirkham, Bussonnier, M., Moore, J., Fulton, A., Bourbeau, J., Onalan, T., Hamman, J., Patel, Z., Rocklin, M., Lee, G.R., Bennett, D., de Andrade, E.S., Abernathey, R., Durant, M., Schut, V., raphael dussin, Barnes, C., Williams, B., Mohar, B., Noyes, C., shikharsg, Nunez-Iglesias, J., Jelenak, A., Banihirwe, A., Baddeley, D., Younkin, E., Sakkis, G., Hunt-Isaak, I., 2021 · 2021
Later among the works it cites.
The open porous media flow reservoir simulator
Rasmussen, A.F., Sandve, T.H., Bao, K., Lauser, A., Hove, J., Skaflestad, B., Klöfkorn, R., Blatt, M., Rustad, A.B., Sævareid, O., et al., 2021 · 2021
Later among the works it cites.
Sleipner 2019 benchmark model
Santi, A.C., Furre, A.K., Nair, K., Ringrose, P., Zweigel, P., · 2021
Later among the works it cites.
Min3p-hpc: a high-performance unstructured grid code for subsurface flow and reactive transport simulation
Su, D., Mayer, K.U., MacQuarrie, K.T., 2021 · 2021
Later among the works it cites.
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Frostig, R., Johnson, M.J., Leary, C., 2018 · 2018
Cited alongside, same era.
Reflecting on the goal and baseline for exascale computing: a roadmap based on weather and climate simulations
Schulthess, T.C., Bauer, P., Wedi, N., Fuhrer, O., Hoefler, T., Schär, C., 2018 · 2018
Cited alongside, same era.
Mesh-tensorflow: Deep learning for supercomputers
Shazeer, N., Cheng, Y., Parmar, N., Tran, D., Vaswani, A., Koanantakool, P., Hawkins, P., Lee, H., Hong, M., Young, C., Sepassi, R., Hechtman, B., 2018 · 2018
Cited alongside, same era.
Dgm: A deep learning algorithm for solving partial differential equations
Sirignano, J., Spiliopoulos, K., 2018 · 2018
Cited alongside, same era.
Fast parallel multidimensional fft using advanced mpi
Dalcin, L., Mortensen, M., Keyes, D.E., 2019 · 2019
Cited alongside, same era.
Devito (v3. 1.0): an embedded domain-specific language for finite differences and geophysical exploration
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Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al., 2019 · 2019
Cited alongside, same era.
Tang, M., Ju, X., Durlofsky, L.J., 2021 · 2021
Later among the works it cites.
U-fno–an enhanced fourier neural operator based-deep learning model for multiphase flow
Wen, G., Li, Z., Azizzadenesheli, K., Anandkumar, A., Benson, S.M., 2021 · 2021
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Later among the works it cites.
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