PDEs
收藏arXiv2025-09-30 收录
下载链接：
https://github.com/georgestod/sparsistent_model_disco/tree/master/deep_learning_based/data
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资源简介：
该数据集包含了低维和高维偏微分方程（PDE）的示例，用于评估神经算子在不同场景下的性能表现。在训练过程中，对于三维示例采用了64x64的分辨率，并在实验中应用了多种插值技术进行下采样和上采样。该数据集的范围涵盖了从低维到高维的各种示例，任务则是利用偏微分方程模拟物理系统。
This dataset contains examples of low-dimensional and high-dimensional partial differential equations (PDEs) for evaluating the performance of neural operators across various scenarios. During training, a resolution of 64×64 was adopted for 3D examples, and multiple interpolation techniques were applied for downsampling and upsampling in the experiments. The dataset covers a broad spectrum of examples ranging from low-dimensional to high-dimensional cases, with the core task being to simulate physical systems using partial differential equations.
