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README.md
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We envision that FlowBench will enable evaluating the interplay between complex geometry, coupled flow phenomena, and data sufficiency on the performance of current, and future, neural PDE solvers. We enumerate several evaluation metrics to help rank order the performance of current (and future) neural PDE solvers. We benchmark the performance of three baseline methods: Fourier Neural Operators (FNO), Convolutional Neural Operators (CNO), and DeepONets. This dataset will be a valuable resource for evaluating neural PDE solvers that model complex fluid dynamics around 2D and 3D objects.
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**Dataset Information**
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*FlowBench* exposes data for four families of datasets:
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We envision that FlowBench will enable evaluating the interplay between complex geometry, coupled flow phenomena, and data sufficiency on the performance of current, and future, neural PDE solvers. We enumerate several evaluation metrics to help rank order the performance of current (and future) neural PDE solvers. We benchmark the performance of three baseline methods: Fourier Neural Operators (FNO), Convolutional Neural Operators (CNO), and DeepONets. This dataset will be a valuable resource for evaluating neural PDE solvers that model complex fluid dynamics around 2D and 3D objects.
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## **Changelog**
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### **(2/2/2025)**
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- **Removed** the `512x128` FPO dataset since the geometry is not properly resolved.
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- **Updated** the `LDC 3D` dataset with 1000 samples at `[128,128,128]`.
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---
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**Dataset Information**
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*FlowBench* exposes data for four families of datasets:
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