Add files using upload-large-folder tool
Browse files- MHD_64.yaml +13 -2
- README.md +17 -52
- data/stats.yaml +20 -0
- data/test/MHD_Ma_0.7_Ms_0.5.hdf5 +2 -2
- data/test/MHD_Ma_0.7_Ms_0.7.hdf5 +2 -2
- data/test/MHD_Ma_0.7_Ms_1.5.hdf5 +2 -2
- data/test/MHD_Ma_0.7_Ms_2.hdf5 +2 -2
- data/test/MHD_Ma_0.7_Ms_7.hdf5 +2 -2
- data/test/MHD_Ma_2_Ms_0.5.hdf5 +2 -2
- data/test/MHD_Ma_2_Ms_0.7.hdf5 +2 -2
- data/test/MHD_Ma_2_Ms_1.5.hdf5 +2 -2
- data/test/MHD_Ma_2_Ms_2.hdf5 +2 -2
- data/test/MHD_Ma_2_Ms_7.hdf5 +2 -2
- data/train/MHD_Ma_0.7_Ms_0.5.hdf5 +2 -2
- data/train/MHD_Ma_0.7_Ms_0.7.hdf5 +2 -2
- data/train/MHD_Ma_0.7_Ms_1.5.hdf5 +2 -2
- data/train/MHD_Ma_0.7_Ms_2.hdf5 +2 -2
- data/train/MHD_Ma_0.7_Ms_7.hdf5 +2 -2
- data/train/MHD_Ma_2_Ms_0.5.hdf5 +2 -2
- data/train/MHD_Ma_2_Ms_0.7.hdf5 +2 -2
- data/train/MHD_Ma_2_Ms_1.5.hdf5 +2 -2
- data/train/MHD_Ma_2_Ms_2.hdf5 +2 -2
- data/train/MHD_Ma_2_Ms_7.hdf5 +2 -2
- data/valid/MHD_Ma_0.7_Ms_0.5.hdf5 +2 -2
- data/valid/MHD_Ma_0.7_Ms_0.7.hdf5 +2 -2
- data/valid/MHD_Ma_0.7_Ms_1.5.hdf5 +2 -2
- data/valid/MHD_Ma_0.7_Ms_2.hdf5 +2 -2
- data/valid/MHD_Ma_0.7_Ms_7.hdf5 +2 -2
- data/valid/MHD_Ma_2_Ms_0.5.hdf5 +2 -2
- data/valid/MHD_Ma_2_Ms_0.7.hdf5 +2 -2
- data/valid/MHD_Ma_2_Ms_1.5.hdf5 +2 -2
- data/valid/MHD_Ma_2_Ms_2.hdf5 +2 -2
- data/valid/MHD_Ma_2_Ms_7.hdf5 +2 -2
- full_stats.yaml +60 -0
- old_and_problematic_data/train/MHDMa_0.7_Ms_0.5.hdf5 +2 -2
- stats.yaml +42 -42
MHD_64.yaml
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boundary_condition_types:
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2: []
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boundary_condition_types:
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n_files: 10
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n_trajectories_per_file:
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n_steps_per_trajectory:
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README.md
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---
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language:
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- en
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license: cc-by-4.0
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tags:
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- physics
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task_categories:
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- time-series-forecasting
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- other
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task_ids:
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- multivariate-time-series-forecasting
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---
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This Dataset is part of <a href="arxiv.org/abs/2412.00568">The Well Collection</a>.
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# How To Load from HuggingFace Hub
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1. Be sure to have `the_well` installed (`pip install the_well`)
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2. Use the `WellDataModule` to retrieve data as follows:
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```python
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from the_well.benchmark.data import WellDataModule
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# The following line may take a couple of minutes to instantiate the datamodule
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datamodule = WellDataModule(
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"hf://datasets/polymathic-ai/",
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"MHD_64",
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)
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train_dataloader = datamodule.train_dataloader()
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for batch in dataloader:
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# Process training batch
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...
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```
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# Magnetohydrodynamics (MHD) compressible turbulence
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**NOTE:** This dataset is available in two different resolutions
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**One line description of the data:** This is an MHD fluid flows in the compressible limit (subsonic, supersonic, sub-Alfvenic, super-Alfvenic).
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**Longer description of the data:** An essential component of the solar wind, galaxy formation, and of interstellar medium (ISM) dynamics is magnetohydrodynamic (MHD) turbulence. This dataset consists of isothermal MHD simulations without self-gravity (such as found in the diffuse ISM) initially generated with resolution
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**Associated paper**: [Paper](https://iopscience.iop.org/article/10.3847/1538-4357/abc484/pdf)
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**Code or software used to generate the data**: Fortran + MPI.
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**Equation**:
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$$
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\begin{align}
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\frac{\partial \rho}{\partial t} + \nabla \cdot (\rho \mathbf{v}) &= 0
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\frac{\partial \rho \mathbf{v}}{\partial t} + \nabla \cdot (\rho \mathbf{v} \mathbf{v} - \mathbf{B} \mathbf{B}) + \nabla p &= 0
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\frac{\partial \mathbf{B}}{\partial t} - \nabla \times (\mathbf{v} \times \mathbf{B}) &= 0
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\end{align}
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$$
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where
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| Dataset | FNO | TFNO | Unet | CNextU-net
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|:-:|:-:|:-:|:-:|:-:|
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| `MHD_64` | 0.3605 | 3561 |0.1798
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Table: VRMSE metrics on test sets (lower is better). Best results are shown in bold. VRMSE is scaled such that predicting the mean value of the target field results in a score of 1.
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**Dimension of discretized data:** 100 timesteps of 64
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**Fields available in the data:** Density (scalar field), velocity (vector field), magnetic field (vector field).
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**Boundary conditions:** periodic boundary conditions.
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**Data are stored separated by (
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**Total time range (
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**Spatial domain size (
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**Set of coefficients or non-dimensional parameters evaluated:** all combinations of
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**Approximate time and hardware used to generate the data:** Downsampled from `MHD_256` after applying ideal low-pass filter.
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-
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**What phenomena of physical interest are catpured in the data:** MHD fluid flows in the compressible limit (sub and super sonic, sub and super Alfvenic).
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# Magnetohydrodynamics (MHD) compressible turbulence
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**NOTE:** This dataset is available in two different resolutions $256^3$ for `MHD_256` and $64^3$ for `MHD_64`. The data was first generated at $256^3$ and then downsampled to $64^3$ after anti-aliasing with an ideal low-pass filter. The data is available in both resolutions.
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**One line description of the data:** This is an MHD fluid flows in the compressible limit (subsonic, supersonic, sub-Alfvenic, super-Alfvenic).
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+
**Longer description of the data:** An essential component of the solar wind, galaxy formation, and of interstellar medium (ISM) dynamics is magnetohydrodynamic (MHD) turbulence. This dataset consists of isothermal MHD simulations without self-gravity (such as found in the diffuse ISM) initially generated with resolution $256^3$ and then downsampled to $64^3$ after anti-aliasing with an ideal low-pass filter. This dataset is the downsampled version.
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**Associated paper**: [Paper](https://iopscience.iop.org/article/10.3847/1538-4357/abc484/pdf)
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**Code or software used to generate the data**: Fortran + MPI.
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**Equation**:
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$$
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\begin{align*}
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\frac{\partial \rho}{\partial t} + \nabla \cdot (\rho \mathbf{v}) &= 0 \\
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\frac{\partial \rho \mathbf{v}}{\partial t} + \nabla \cdot (\rho \mathbf{v} \mathbf{v} - \mathbf{B} \mathbf{B}) + \nabla p &= 0 \\
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\frac{\partial \mathbf{B}}{\partial t} - \nabla \times (\mathbf{v} \times \mathbf{B}) &= 0
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\end{align*}
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$$
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where $\rho$ is the density, $\mathbf{v}$ is the velocity, $\mathbf{B}$ is the magnetic field, $\mathbf{I}$ the identity matrix and $p$ is the gas pressure.
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| Dataset | FNO | TFNO | Unet | CNextU-net
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|:-:|:-:|:-:|:-:|:-:|
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| `MHD_64` | 0.3605 | 3561 |0.1798|$\mathbf{0.1633}$|
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Table: VRMSE metrics on test sets (lower is better). Best results are shown in bold. VRMSE is scaled such that predicting the mean value of the target field results in a score of 1.
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## About the data
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**Dimension of discretized data:** 100 timesteps of 64 $\times$ 64 $\times$ 64 cubes.
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**Fields available in the data:** Density (scalar field), velocity (vector field), magnetic field (vector field).
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**Boundary conditions:** periodic boundary conditions.
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**Data are stored separated by ($\Delta t$):** 0.01 (arbitrary units).
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**Total time range ($t\_{min}$ to $t\_{max}$):** $t\_{min} = 0$, $t\_{max} = 1$.
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**Spatial domain size ($L_x$, $L_y$, $L_z$):** dimensionless so 64 pixels.
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**Set of coefficients or non-dimensional parameters evaluated:** all combinations of $\mathcal{M}_s=${0.5, 0.7, 1.5, 2.0 7.0} and $\mathcal{M}_A =${0.7, 2.0}.
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**Approximate time and hardware used to generate the data:** Downsampled from `MHD_256` after applying ideal low-pass filter.
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## What is interesting and challenging about the data:
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**What phenomena of physical interest are catpured in the data:** MHD fluid flows in the compressible limit (sub and super sonic, sub and super Alfvenic).
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data/stats.yaml
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mean:
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density: 1.0015409125682395
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magnetic_field:
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- 0.533287413173605
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- 8.496519008201078e-10
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- 1.9938109340438524e-08
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velocity:
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- 0.0096151666910381
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- 0.06783284226761686
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- 0.01931474557406926
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std:
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density: 0.8922789419164839
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magnetic_field:
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- 0.5286711156725538
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- 0.31708956322212195
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- 0.32008268824591013
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velocity:
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- 0.4851500418364637
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- 0.43813973615708446
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- 0.4785808622773084
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data/test/MHD_Ma_0.7_Ms_0.5.hdf5
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