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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:    TypeError
Message:      Couldn't cast array of type
struct<physics_integrator_nn: string>
to
{}
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
              struct<physics_integrator_nn: string>
              to
              {}

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AgentFEM Material Loading Memory

Material loading-memory hysteresis

An open, reproducible research dataset for path-dependent material modeling, neural constitutive surrogates and finite-element deployment tests. The repository contains four related, explicitly separated research protocols.

Releases

  • T2 v1: 1,008 proportional cyclic J2 trajectories for loading-memory baselines and forward/reversed ordering controls.
  • T2 multiaxial v2: 1,024 independently generated J2 and Chaboche trajectories in the complete five-dimensional deviatoric strain space, with ID, path-OOD and parameter-OOD splits.
  • T2 DENIM closure v1: 128 multiaxial trajectories from a deliberately richer three-memory, tabulated-hardening reference material. The published two-memory DENIM model must learn the missing internal-variable closure.
  • T2 DENIM boundary v1: 500 additional trajectories for training-density, path, amplitude, long-history and integration-resolution boundaries.

The repository therefore contains 2,660 complete trajectories. Releases are used as a staged evidence chain; they are not presented as 2,660 exchangeable samples from one training distribution.

Start with DENIM_START_HERE.md for the incomplete-physics study, T2_V2_START_HERE.md for the multiaxial benchmark, or START_HERE.md for the original v1 release.

Material-conditioned DENIM v2 protocol

All 2,660 trajectories now have a frozen role in the conditional study: 1,484 train, 407 validation and 769 test. The release adds no duplicate data. Shared known-material pretraining plus incomplete-material replay reduces long-history RMSE from 10.906 to 4.263 MPa. Reproduction code, exact accounting and complete metrics are in conditional_v2/.

DENIM incomplete-physics closure

DENIM closure summary

DENIM stands for Discrete-Energy Neural Internal-variable Model. The reference material contains three kinematic memory channels and a non- exponential tabulated isotropic-hardening curve. DENIM retains only two memory channels and receives none of the reference hardening equations or parameters.

Held-out non-proportional path results:

Model Test RMSE Test R2
Incomplete J2, no learned hardening 59.541 MPa 0.730208
GRU 76.988 MPa 0.548933
DENIM 1.136 MPa 0.999902

The 918-parameter DENIM also passed coarse/fine increment checks and three notched-bar deployment gates. Cyclic and monotonic reaction relative-L2 errors were 0.636% and 0.500%; the severe cyclic stress test required one global trust-region fallback. This remains a fixed synthetic material study with internal-state supervision, not an experimental calibration or a certified production material.

The standalone weights and model card are published at HaomingLuo/AgentFEM-DENIM.

DENIM capability-boundary extension

DENIM boundary comparison

The 500-trajectory extension contains 250 training, 50 validation, 80 held-out path, 60 amplitude-extrapolation, 40 long-history and 20 matched-resolution histories. No failed case was silently removed.

Test Incomplete J2 GRU Frozen DENIM Expanded DENIM
Published held-out paths 59.541 98.260 1.136 0.714
New path OOD 58.177 96.959 1.163 0.743
Amplitude OOD 69.315 95.896 3.091 2.294
Long-history stress test 54.394 90.757 11.261 10.906

Values are stress RMSE in MPa. The long-history result is reported as a current capability boundary, not hidden by the stronger ordinary path results. Expanded-DENIM RMSE on matched 61/121/481/961-state histories was 0.673/0.722/0.761/0.767 MPa.

  • Viewer-friendly index: data/t2_denim_boundary_v1/index.csv
  • Lossless trajectories: data/t2_denim_boundary_v1/cohort.h5
  • Data card: data/t2_denim_boundary_v1/README.md
  • Quality report: artifacts/t2_denim_boundary_v1/QUALITY_REPORT.md
  • Full metrics: artifacts/t2_denim_boundary_v1/model_metrics.json
  • All-data usage: docs/T2_ALL_DATA_STAGE_SUMMARY.md

AgentFEM runtime validation

The expanded DENIM checkpoint is also available as a checksum-authenticated safetensors bundle in the AgentFEM-DENIM model repository. The safe bundle reproduces the legacy implementation on the fixed 121-step path to a maximum stress difference of 2.68e-7 Pa and identical final PEEQ.

Serial and two-rank AgentFEM implicit plastic-bar runs both completed 4/4 increments, with a reported maximum-stress difference of 5.96e-8 Pa between the two executions. The current plastic automatic-differentiation tangent has a documented 0.05–0.80% discrepancy against fixed-old-state finite differences over the audited plastic states. The demonstrated global cases converge; an exact consistent plastic tangent remains outside the present claim boundary.

  • Machine evidence: artifacts/t2_denim_agentfem_v1/runtime_validation.json
  • Validation note: artifacts/t2_denim_agentfem_v1/RUNTIME_VALIDATION.md
  • Data/model handoff: docs/T2_DATA_MODEL_HANDOFF.md

Multiaxial v2 at a glance

  • eight path families, including non-proportional, rotating and random five-direction loading;
  • 241 ordered states per trajectory;
  • stress, strain, plastic strain, PEEQ, two-family total backstress, plastic increments and physical diagnostics;
  • zero quality-gate failures across all 1,024 trajectories;
  • six model families evaluated under three frozen protocols;
  • element-level deployment in a controlled notched-bar finite-element gate.

Multiaxial hysteresis examples

Stress RMSE on held-out trajectories:

Model ID Path OOD Parameter OOD
Pointwise MLP 83.51 MPa 105.81 MPa 82.34 MPa
GRU 17.26 MPa 76.03 MPa 19.26 MPa
LSTM 22.67 MPa 102.54 MPa 22.89 MPa
Causal TCN 21.82 MPa 60.44 MPa 21.99 MPa
Physics-state GRU 23.06 MPa 66.00 MPa 24.19 MPa
Physics-integrator NN 0.0128 MPa 0.0237 MPa 0.0275 MPa

Model comparison

The physics-integrator NN predicts a bounded plastic-multiplier correction and then applies differentiable J2/Chaboche consistency corrections while storing plastic strain, PEEQ and two backstress tensors. It passed the mild and severe structural gates with reaction relative-L2 errors of 1.65e-7 and 2.39e-7. The severe case recorded one trust-region fallback at complete unloading.

This is a white-box physics-fusion ceiling: the embedded return-mapping equations match the constitutive families that generated the synthetic data. It demonstrates the benefit of architecture-level physics for this controlled benchmark, but does not establish transfer to unknown materials or misspecified physical models.

Reproduction and evidence

  • Dataset details: data/t2_multiaxial_ood_v2/README.md
  • Model card: models/t2_multiaxial_ood_v2/README.md
  • Quality report: artifacts/t2_multiaxial_ood_v2/QUALITY_REPORT.md
  • Full metrics: artifacts/t2_multiaxial_ood_v2/model_metrics.json
  • Structural gates: artifacts/t2_multiaxial_ood_v2/structural_validation/
  • Manuscript roadmap: docs/T2_MULTIAXIAL_RESEARCH_MEMO.md
  • Literature map: docs/T2_MULTIAXIAL_LITERATURE.md
  • Commands: T2_V2_REPRODUCE.md
  • DENIM data and evidence: DENIM_START_HERE.md
  • DENIM reproduction: DENIM_REPRODUCE.md

The data were generated with AgentFEM commit 058faecc05aeda143d014fd229401003a9258bbb (0.3.7.dev0). All quantities use SI units. Voigt order is xx, yy, zz, xy, yz, xz, with tensor shear.

Scope

The releases are synthetic, small-strain material-point benchmarks. They do not constitute experimental calibration, fatigue-life prediction, damage, finite-strain plasticity or a production-certified learned material. Data are CC BY 4.0; code files are covered by the included code license.

Evidence protocol v3

See article_evidence_v3/ for frozen ablation and leave-one-family-out split definitions.

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