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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 six staged, explicitly separated releases.

Start here

Goal Recommended entry
Understand the current dataset This page and the sealed-test data card
Train or benchmark a constitutive model DENIM start guide
Reproduce the multiaxial baseline study Multiaxial v2 guide
Reproduce the original loading-memory release Original v1 guide
Use the latest DENIM checkpoint AgentFEM-DENIM

Current trajectory accounting:

Role Trajectories Interpretation
Training 1,808 Optimization data for the current v2.2 model
Validation 503 Checkpoint selection and anti-forgetting guardrails
Development test 1,349 Frozen historical benchmarks; later aggregate results informed subsequent model design
Prospective sealed test 128 Published after model freeze; never used for training or selection
Total 3,788 Complete histories, not exchangeable independent experiments

What is a loading history, and why does it matter?

A loading history is the ordered sequence of deformation applied to a material. For an elastic spring, only the present deformation matters. For an elastoplastic metal, the route matters as well: loading, unloading, reversing, rotating the loading direction or adding a mean strain changes the material's internal state. Two specimens can therefore arrive at the same current strain with different stresses because they arrived there by different routes.

This dataset varies the history deliberately rather than merely sampling more points on one curve:

History variation Question being tested
Proportional and reversed cycles Can the model reproduce yielding, unloading and the Bauschinger effect?
Non-proportional and rotating multiaxial paths Can it track changing stress directions and coupled material memory?
Nested minor loops and non-periodic sequences Can it remember partial unload/reload events instead of resetting its state?
Amplitude and mean shifts Can it extrapolate when the load becomes larger or biased to one side?
100--200-cycle histories Does a small state error accumulate into long-term drift?
Matched coarse/fine discretizations Is the prediction tied to the physical path or to a particular step size?
Material-parameter variation Can one conditioned model represent a family of materials rather than one fixed curve?

The scientific object is therefore not an isolated stress value. It is the mapping from material parameters + ordered loading history + current internal state to the next stress and state. Randomly shuffling time steps or mixing related resolution groups across data roles would destroy this meaning.

Recommended evaluation metrics

Report absolute stress RMSE in MPa together with a scale-free metric. MPa is needed for engineering interpretation; a relative metric makes results easier to compare across cohorts and stress levels.

  • Relative L2 error (%) = ||stress_pred - stress_ref||2 / ||stress_ref||2 × 100. This is the preferred scale-free trajectory/state metric for signed cyclic stresses.
  • Yield-normalized RMSE (%) = RMSE / reference_yield_stress × 100. This answers how large the average error is relative to the 280 MPa reference yield stress used by the sealed protocol.
  • R2 is useful as a supplementary global fit indicator, but should not replace an engineering error in MPa.
  • MAPE is not recommended because cyclic stress repeatedly crosses zero; division by values near zero can make an accurate prediction look arbitrarily poor.

For the sealed reference data, the stress RMS scales are 359.540 MPa for global path OOD, 91.411 MPa for 150/200-cycle histories, 371.687 MPa for amplitude and mean shifts, and 206.814 MPa over all 128 trajectories. These denominators are published so every model can be compared under the same convention. Model scores remain in the model repository to keep this page focused on the data and evaluation contract.

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.
  • T2 DENIM capability v4: 1,000 trajectories for 100-cycle horizons, full six-component loading, AgentFEM-derived structural paths and paired discretization/noisy-observation tests.
  • T2 DENIM sealed test v1: 128 prospectively generated trajectories for globally unseen paths, 150/200-cycle horizons and combined amplitude/mean shift. The DENIM v2.2 weights and evaluation protocol were frozen first.

The original 2,660-role protocol remains frozen; capability v4 adds its own roles and the sealed release adds no optimization data. The six releases form a staged evidence chain and must not be pooled or randomly reshuffled without respecting material, path-family, resolution-group and release boundaries.

Historical Path-OOD labels are protocol-relative: they mean unseen for the model and training split defined at that release. Some historical test-family names entered later training releases, so those scores are not global OOD claims for v2.2. sealed_test_v1 is the current prospective global path-OOD protocol.

Historical 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/.

Historical fixed-material DENIM 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

DENIM capability extension v4

Capability extension preview

The v4 increment adds 1,000 complete trajectories: 300 long-cycle, 300 full six-component non-proportional, 200 AgentFEM-derived local structural paths and 200 matched discretization/observation trajectories. The 100-cycle histories contain 2,001 accepted material states. Fifty continuous paths are integrated at 61, 121, 241 and 481 states and carry explicit group IDs; noisy and sparse observations are fields paired with clean truth, not separately counted cases.

The structure-derived cohort uses local strain bases from twelve AgentFEM linear-elastic unit-load solves on four perforated/notched plates, followed by nonlinear reference-material replay. It does not claim coupled elastoplastic redistribution. See data/t2_denim_capability_v4/README.md for the exact protocol and artifacts/t2_denim_capability_v4/capability_summary.json for machine-readable boundary statistics.

Prospective sealed test v1

The 128-trajectory sealed test is a final, non-training extension: 64 globally unseen path-shape histories, 32 histories with 150 or 200 cycles, and 32 combined amplitude/mean-shift histories. All eight path-family identifiers are disjoint from training. The reference integrator enforces finite values, monotone accumulated plastic strain, a relative yield-surface residual below 1e-8, total equivalent strain no greater than 0.0155, and accumulated equivalent plastic strain no greater than 0.10.

The manifest binds the data to DENIM v2.2 weights SHA-256 d7b848f41aa46616c76c5f0db536d8ecfbc508a5375faa3cbbcc555fb062633b. These samples are ineligible for training, validation, checkpoint selection or model redesign. The repository-level protocol audit reports zero exact cross-role collisions for full model inputs and for input/target pairs. This dataset release intentionally contains no sealed-test model score; evaluation is published separately in the model repository.

  • Data card: data/t2_denim_sealed_test_v1/README.md
  • Lossless trajectories: data/t2_denim_sealed_test_v1/cohort.h5
  • Viewer indexes: data/t2_denim_sealed_test_v1/index.csv and index.jsonl
  • Generation contract: configs/t2_denim_sealed_test_v1.json
  • Leakage audit: artifacts/t2_denim_protocol_audit_v1.json

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 original multiaxial release used AgentFEM commit 058faecc05aeda143d014fd229401003a9258bbb (0.3.7.dev0). Later release manifests record their own software revisions and hashes. All quantities use SI units. Voigt order is xx, yy, zz, xy, yz, xz, with tensor shear.

Scope

The releases are controlled synthetic, small-strain benchmarks for J2/Chaboche families, material-parameter variation and one deliberately hidden three-memory reference material. They are not a general metals database and do not constitute experimental calibration, fatigue-life prediction, damage, finite-strain plasticity or a production-certified learned material. Complete internal states are privileged simulation labels, not quantities normally available from standard material tests. 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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