Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary Domains
Paper • 2505.18781 • Published
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Datasets of GAOT
This repository contains the datasets used in the paper
"Geometry Aware Operator Transformer as an Efficient and Accurate Neural Surrogate for PDEs on Arbitrary Domains."
Paper: https://arxiv.org/abs/2505.18781
The repository includes two subfolders:
time_dep/— time-dependent datasetstime_indep/— time-independent datasets
In addition, some datasets used in the paper are provided by RIGNO (see their paper for details).
These can be downloaded from Zenodo.
For a detailed description of all datasets, please refer to Supplementary Material, Table D.1 of the paper.
Data Structure
Each file contains either two or three groups:
- u: the vector-valued solution function
- c: the vector-valued known spatial parameters (constant in time)
- x: the spatial coordinates in all directions (e.g., x and y)
All arrays are 4-dimensional with the following axis order:[batch, time, space, variables].
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