Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning (Dataset)
Creators
- 1. NVIDIA
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2.
California Institute of Technology
Description
We present the datasets for the paper "Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning." In this paper, we identify and distill the key principles for constructing practical implementations of mappings between infinite-dimensional function spaces. Using these principles, we propose a recipe for converting several popular neural architectures into neural operators with minimal modifications.
Our paper includes experiments on a Navier-Stokes dataset at several resolutions: 64x64, 128x128, 256x256, 512x512 and 1024x1024. This repository contains the exact training/testing split that we used in training and evaluation for our experiments. The code in our GitHub repository preprocesses and constructs the training/testing data from the raw datasets above.
Notes
M. Liu-Schiaffini is supported in part by the Mellon Mays Undergraduate Fellowship. A. Anandkumar is supported in part by Bren endowed chair, ONR (MURI grant N00014-18- 12624), and by the AI2050 senior fellow program at Schmidt Sciences.
Files
Files
(25.2 GB)
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md5:b5c29ccac9cebb3fc3491ec4dc96f7e4
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md5:585cfe69e3e210c8bf4900b1e0ba3c2b
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md5:8f9e3bc31c91cfdf6f72bc806e78dd4c
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262.1 MB | Download |
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md5:b004551e3293f78560971b8baa62165d
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1.3 GB | Download |
Additional details
Software
- Repository URL
- https://github.com/neuraloperator/NNs-to-NOs