The Cost-Accuracy Trade-Off In Operator Learning With Neural Networks
Description
The repo contains data for the paper @article{de2022cost, title={The Cost-Accuracy Trade-Off In Operator Learning With Neural Networks}, author={De Hoop, Maarten and Huang, Daniel Zhengyu and Qian, Elizabeth and Stuart, Andrew M}, journal={arXiv preprint arXiv:2203.13181}, year={2022} }. The data set is generated to study different neural networks for AI + engineering. The data set contains simulation data for the following 4 benchmark problems 1) Navier stokes equation : NavierStokes_inputs.npy & NavierStokes_outputs.npy. 2) Helmholtz equation : Helmholtz_inputs.npy & Helmholtz_outputs.npy. 3) Structural mechanics equation : StructuralMechanics_inputs.npy & StructuralMechanics_outputs.npy. 4) Advection equation : Advection_inputs.npy & Advection_outputs.npy. The data are stored as nx by ny by ndata arrays (2d problems) or nx by ndata arrays (1d problems).
Files
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md5:5c6d91ab9289baf7140c9e00fed8ac42
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537.9 MB | Download |
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md5:c973ccf9a8431d83a6886bb3cf2c29c9
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64.0 MB | Download |
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md5:fad90eaee4aeacea1a5e5adf3cb131d4
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64.0 MB | Download |
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md5:0eccb57ef29d7fd370cd8832dedeb5ca
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3.3 GB | Download |
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md5:5bd21256ae4001b3dcfdfd44df18c9fc
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3.3 GB | Download |
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md5:eac20ee38680bc938bb8d76453dae6df
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1.3 GB | Download |
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md5:9fcc57f7ffb5ff24e248e59cfada38fa
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1.3 GB | Download |
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md5:c4032525b4763adb3f89857b8b28efd2
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537.9 MB | Download |
Additional details
- CALTECHDATA_ID
- 20091