Rethinking Forward Processes for Score-Based Data Assimilation in High Dimensions
Paper • 2604.02889 • Published
Dataset Viewer
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
batch: int64
state_shape: list<item: int64>
child 0, item: int64
dtype: string
device: string
saved_steps: list<item: int64>
child 0, item: int64
dynamics_type: string
dim: int64
num_samples: int64
saved_for: string
initial_step: int64
note: string
to
{'batch': Value('int64'), 'state_shape': List(Value('int64')), 'dtype': Value('string'), 'device': Value('string'), 'saved_steps': List(Value('int64')), 'note': Value('string')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 129, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 544, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 3052, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2559, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2584, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 558, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 440, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
batch: int64
state_shape: list<item: int64>
child 0, item: int64
dtype: string
device: string
saved_steps: list<item: int64>
child 0, item: int64
dynamics_type: string
dim: int64
num_samples: int64
saved_for: string
initial_step: int64
note: string
to
{'batch': Value('int64'), 'state_shape': List(Value('int64')), 'dtype': Value('string'), 'device': Value('string'), 'saved_steps': List(Value('int64')), 'note': Value('string')}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
MASF datasets
Prior ensembles used in Rethinking Forward Processes for Score-Based Nonlinear Data Assimilation in High Dimensions. They are used to pretrain the score models and to initialize the filter ensemble.
Code: https://github.com/tcnllab-oss/masf · Checkpoints: eunbii1/masf
Files
| directory | system | state shape | step |
|---|---|---|---|
datasets/kolmogorov/dim128_num_samples1000 |
Kolmogorov flow, Re 1000, dt 0.2 | (1000, 2, 128, 128) | 50 |
datasets/kolmogorov/dim256_num_samples1000 |
Kolmogorov flow, Re 1000, dt 0.2 | (1000, 2, 256, 256) | 50 |
datasets/kolmogorov/dim512_num_samples1000 |
Kolmogorov flow, Re 1000, dt 0.2 | (1000, 2, 512, 512) | 50 |
datasets/lorenz96/dim40_num_samples1000 |
Lorenz96, F 8, dt 0.05 | (1000, 1, 40) | 25 |
Each directory has step_XXXXXX.pt and meta.json. The .pt file is a dict {"step": int, "state": float32 tensor}. Kolmogorov states are velocity fields (u, v).
Usage
hf download eunbii1/masf --repo-type dataset --include "datasets/*" --local-dir .
import torch
state = torch.load("datasets/lorenz96/dim40_num_samples1000/step_000025.pt")["state"]
Run this from the root of the code repo. The code finds datasets/ there automatically.
Citation
@article{yoon2026masf,
title={Rethinking Forward Processes for Score-Based Nonlinear Data Assimilation in High Dimensions},
author={Yoon, Eunbi and Chang, Won and Kim, Donghan and Kim, Dae Wook},
journal={arXiv preprint},
year={2026}
}
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