The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type
string
to
{'bytes': Value('int64'), 'sha256': Value('string')}
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, 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 127, 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 483, 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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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 343, 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 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2312, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1861, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2158, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
string
to
{'bytes': Value('int64'), 'sha256': Value('string')}Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
AgentFEM T3 Controlled Transient Heat
This dataset contains 192 complete finite-element trajectories from 64 independent physical scenarios. A two-dimensional metallic solid is heated by three localized volumetric sources and cooled by convection along its exterior. Every scenario branches from the same state at 300 s into three possible futures: maintain source power, throttle all sources to 40%, or shut down.
Why the branches matter
An ordinary transient dataset shows what happened under one command sequence. T3 also stores what would have happened from the same physical state under alternative commands. It therefore supports full-field forecasting, hotspot prediction, reduced-order modeling, operator learning, state estimation and model-predictive-control research.
Data
- 64 independent combinations of conductivity, density, heat capacity, convection and three source layouts;
- 3 counterfactual branches per scenario;
- 61 accepted states over 0–600 s;
- one fixed 36×24 quadrilateral mesh with 925 CG1 temperature degrees of freedom;
- full temperature fields, three controls, five sensors, hotspot coordinates, heat content, applied heat rate, outward heat rate and balance residual;
- SI units throughout.
cohort.h5 stores the complete arrays, index.csv and index.jsonl provide trajectory-level metadata, quality.json records the scientific gates, and preview.png shows one complete counterfactual triplet.
| Array | Shape per trajectory | Unit / meaning |
|---|---|---|
temperature_history_k |
61 × 925 | nodal temperature, K |
source_control_w_m3 |
61 × 3 | Gaussian-source peak power density, W/m³ |
sensor_temperature_k |
61 × 5 | fixed virtual sensors, K |
hotspot_coordinates_m |
61 × 2 | hottest nodal location, m |
thermal_content |
61 | sensible thermal content per unit depth, J/m |
applied_heat_rate |
61 | applied heat rate per unit depth, W/m |
outward_heat_rate |
61 | outward heat rate per unit depth, W/m |
heat_balance_residual |
61 | discrete balance residual per unit depth, J/m |
The sampled ranges are conductivity 120–220 W/(m·K), density 2500–3200 kg/m³, specific heat 700–1000 J/(kg·K), convection 10–40 W/(m²·K), source width 4–9 mm and source peak power density 2–8 MW/m³. These are bounded synthetic descriptors, and every realized value and source location is stored in the trajectory metadata.
Leakage-safe split
The 64 scenario groups are split 48/8/8 into training, validation and test. Their 192 trajectories are therefore 144/24/24. All three branches from one physical scenario remain in the same split, so an identical pre-intervention field never appears on both sides of the evaluation boundary.
Verification
- 192/192 trajectories completed with finite fields;
- largest cohort heat-balance relative residual:
1.409e-11; - maximum temperature/control difference before branching: exactly zero;
- manufactured-solution refined relative L2 error:
0.2605%; - time-step halving changes final sensor values by
0.2415%; - mesh doubling changes final sensor values by
0.0023%.
Baseline
A validation-selected PCA/ridge autoregressive state-space model obtains test full-field RMSE 9.747 K, normalized temperature-rise RMSE 19.72%, and maximum-temperature MAE 6.207 K. Persistence gives 49.420 K. The baseline is deliberately simple and defines a reproducible lower bar rather than a claimed best model.
Scope
T3 v1 is a controlled solid-conduction benchmark. It uses constant isotropic thermal properties within each trajectory, a synthetic metallic parameter range and unit out-of-plane thickness. It does not include radiation, phase change, fluid flow or thermo-mechanical feedback. Those effects belong to later, separately versioned datasets.
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