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README.md
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physics-informed machine learning, neural operators, and
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physics foundation models.
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Currently, the dataset contains CFD field variables including:
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- velocity
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- pressure
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| pressure | Pressure field |
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| temperature | Temperature field |
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## Applications
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This dataset can be used for:
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- Physics-informed neural networks (PINNs)
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- Neural operators (FNO, DeepONet)
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- Physics transformers
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- Thermofluid foundation models
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- CFD surrogate modeling
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## Dataset Structure
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```
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└── case_0003.csv
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```
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## License
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Apache License 2.0
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physics-informed machine learning, neural operators, and
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physics foundation models.
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Currently, the dataset contains simulation-based CFD field variables including:
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- velocity
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- pressure
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| pressure | Pressure field |
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| temperature | Temperature field |
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## Dataset Structure
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```
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└── case_0003.csv
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```
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## Parameters
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Each simulation case includes operating conditions:
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- Reynolds number (Re)
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- Prandtl number (Pr)
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- Inlet velocity
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- Inlet temperature
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## Applications
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This dataset can be used for:
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- Physics-informed neural networks (PINNs)
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- Neural operators (FNO, DeepONet)
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- Physics transformers
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- Thermofluid foundation models
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- CFD surrogate modeling
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## Version
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### v0.1 - Initial demonstration dataset
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Contents:
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- 3 simulation cases
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- Velocity, pressure, and temperature fields
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- Tabular CFD representation
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## License
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Apache License 2.0
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