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MHD_64.yaml CHANGED
@@ -25,8 +25,19 @@ constant_field_names:
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  2: []
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  boundary_condition_types:
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  - PERIODIC
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- n_simulations: 10
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- n_steps_per_simulation:
 
 
 
 
 
 
 
 
 
 
 
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  - 100
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  - 100
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  - 100
 
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  2: []
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  boundary_condition_types:
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  - PERIODIC
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+ n_files: 10
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+ n_trajectories_per_file:
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+ - 1
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+ - 1
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+ - 1
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+ - 1
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+ - 1
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+ - 1
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+ n_steps_per_trajectory:
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  - 100
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  - 100
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  - 100
README.md CHANGED
@@ -1,46 +1,10 @@
1
- ---
2
- language:
3
- - en
4
- license: cc-by-4.0
5
- tags:
6
- - physics
7
-
8
- task_categories:
9
- - time-series-forecasting
10
- - other
11
- task_ids:
12
- - multivariate-time-series-forecasting
13
- ---
14
-
15
- This Dataset is part of <a href="arxiv.org/abs/2412.00568">The Well Collection</a>.
16
-
17
- # How To Load from HuggingFace Hub
18
-
19
- 1. Be sure to have `the_well` installed (`pip install the_well`)
20
- 2. Use the `WellDataModule` to retrieve data as follows:
21
-
22
- ```python
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- from the_well.benchmark.data import WellDataModule
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-
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- # The following line may take a couple of minutes to instantiate the datamodule
26
- datamodule = WellDataModule(
27
- "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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-
32
- for batch in dataloader:
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- # Process training batch
34
- ...
35
- ```
36
-
37
  # Magnetohydrodynamics (MHD) compressible turbulence
38
 
39
- **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.
40
 
41
  **One line description of the data:** This is an MHD fluid flows in the compressible limit (subsonic, supersonic, sub-Alfvenic, super-Alfvenic).
42
 
43
- **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.
44
 
45
  **Associated paper**: [Paper](https://iopscience.iop.org/article/10.3847/1538-4357/abc484/pdf)
46
 
@@ -49,27 +13,28 @@ for batch in dataloader:
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  **Code or software used to generate the data**: Fortran + MPI.
50
 
51
  **Equation**:
 
52
  $$
53
- \begin{align}
54
- \frac{\partial \rho}{\partial t} + \nabla \cdot (\rho \mathbf{v}) &= 0 \nonumber\\
55
- \frac{\partial \rho \mathbf{v}}{\partial t} + \nabla \cdot (\rho \mathbf{v} \mathbf{v} - \mathbf{B} \mathbf{B}) + \nabla p &= 0 \nonumber\\
56
- \frac{\partial \mathbf{B}}{\partial t} - \nabla \times (\mathbf{v} \times \mathbf{B}) &= 0 \nonumber\\
57
- \end{align}
58
  $$
59
 
60
- 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.
61
 
62
  ![Gif](https://users.flatironinstitute.org/~polymathic/data/the_well/datasets/MHD_64/gif/density_unnormalized.gif)
63
 
64
  | Dataset | FNO | TFNO | Unet | CNextU-net
65
  |:-:|:-:|:-:|:-:|:-:|
66
- | `MHD_64` | 0.3605 | 3561 |0.1798|**0.1633**|
67
 
68
  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.
69
 
70
- # About the data
71
 
72
- **Dimension of discretized data:** 100 timesteps of 64 \\(\times\\) 64 \\(\times\\) 64 cubes.
73
 
74
  **Fields available in the data:** Density (scalar field), velocity (vector field), magnetic field (vector field).
75
 
@@ -83,17 +48,17 @@ Table: VRMSE metrics on test sets (lower is better). Best results are shown in b
83
 
84
  **Boundary conditions:** periodic boundary conditions.
85
 
86
- **Data are stored separated by ( \\(\Delta t\\)):** 0.01 (arbitrary units).
87
 
88
- **Total time range ( \\(t\_{min}\\) to \\(t\_{max}\\)):** \\(t\_{min} = 0\\), \\(t\_{max} = 1\\).
89
 
90
- **Spatial domain size ( \\(L_x\\), \\(L_y\\), \\(L_z\\)):** dimensionless so 64 pixels.
91
 
92
- **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}.
93
 
94
  **Approximate time and hardware used to generate the data:** Downsampled from `MHD_256` after applying ideal low-pass filter.
95
 
96
- # What is interesting and challenging about the data:
97
 
98
  **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).
99
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
  # Magnetohydrodynamics (MHD) compressible turbulence
2
 
3
+ **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.
4
 
5
  **One line description of the data:** This is an MHD fluid flows in the compressible limit (subsonic, supersonic, sub-Alfvenic, super-Alfvenic).
6
 
7
+ **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.
8
 
9
  **Associated paper**: [Paper](https://iopscience.iop.org/article/10.3847/1538-4357/abc484/pdf)
10
 
 
13
  **Code or software used to generate the data**: Fortran + MPI.
14
 
15
  **Equation**:
16
+
17
  $$
18
+ \begin{align*}
19
+ \frac{\partial \rho}{\partial t} + \nabla \cdot (\rho \mathbf{v}) &= 0 \\
20
+ \frac{\partial \rho \mathbf{v}}{\partial t} + \nabla \cdot (\rho \mathbf{v} \mathbf{v} - \mathbf{B} \mathbf{B}) + \nabla p &= 0 \\
21
+ \frac{\partial \mathbf{B}}{\partial t} - \nabla \times (\mathbf{v} \times \mathbf{B}) &= 0
22
+ \end{align*}
23
  $$
24
 
25
+ 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.
26
 
27
  ![Gif](https://users.flatironinstitute.org/~polymathic/data/the_well/datasets/MHD_64/gif/density_unnormalized.gif)
28
 
29
  | Dataset | FNO | TFNO | Unet | CNextU-net
30
  |:-:|:-:|:-:|:-:|:-:|
31
+ | `MHD_64` | 0.3605 | 3561 |0.1798|$\mathbf{0.1633}$|
32
 
33
  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.
34
 
35
+ ## About the data
36
 
37
+ **Dimension of discretized data:** 100 timesteps of 64 $\times$ 64 $\times$ 64 cubes.
38
 
39
  **Fields available in the data:** Density (scalar field), velocity (vector field), magnetic field (vector field).
40
 
 
48
 
49
  **Boundary conditions:** periodic boundary conditions.
50
 
51
+ **Data are stored separated by ($\Delta t$):** 0.01 (arbitrary units).
52
 
53
+ **Total time range ($t\_{min}$ to $t\_{max}$):** $t\_{min} = 0$, $t\_{max} = 1$.
54
 
55
+ **Spatial domain size ($L_x$, $L_y$, $L_z$):** dimensionless so 64 pixels.
56
 
57
+ **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}.
58
 
59
  **Approximate time and hardware used to generate the data:** Downsampled from `MHD_256` after applying ideal low-pass filter.
60
 
61
+ ## What is interesting and challenging about the data:
62
 
63
  **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).
64
 
data/stats.yaml ADDED
@@ -0,0 +1,20 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ mean:
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+ density: 1.0015409125682395
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+ magnetic_field:
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+ - 8.496519008201078e-10
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+ - 1.9938109340438524e-08
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+ velocity:
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+ - 0.06783284226761686
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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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