Error code:   JWTInvalidSignature
Exception:    InvalidSignatureError
Message:      Signature verification failed
Traceback:    Traceback (most recent call last):
                File "/src/libs/libapi/src/libapi/jwt_token.py", line 286, in validate_jwt
                  decoded = jwt.decode(
                      jwt=token,
                  ...<2 lines>...
                      options=options,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 368, in decode
                  decoded = self.decode_complete(
                      jwt,
                  ...<8 lines>...
                      leeway=leeway,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jwt.py", line 265, in decode_complete
                  decoded = self._jws.decode_complete(
                      jwt,
                  ...<3 lines>...
                      detached_payload=detached_payload,
                  )
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 270, in decode_complete
                  self._verify_signature(
                  ~~~~~~~~~~~~~~~~~~~~~~^
                      signing_input,
                      ^^^^^^^^^^^^^^
                  ...<4 lines>...
                      options=merged_options,
                      ^^^^^^^^^^^^^^^^^^^^^^^
                  )
                  ^
                File "/usr/local/lib/python3.14/site-packages/jwt/api_jws.py", line 417, in _verify_signature
                  raise InvalidSignatureError("Signature verification failed")
              jwt.exceptions.InvalidSignatureError: Signature verification failed

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Discrete Elastic Rods Simulation Dataset

Dataset Description

This dataset contains synthetic data generated from Discrete Elastic Rods (DER) simulations, a physical model used to represent deformable slender structures such as hair strands, ropes, cables, and elastic fibers.

The dataset was generated frame-by-frame during physical simulations and stores geometric, kinematic, and dynamic properties for each rod vertex.

The primary objective of this dataset is to support machine learning research involving:

Graph Neural Networks (GNNs)
Physics-informed learning
Dynamics prediction
Physical regression
Deformable object simulation

Each sample corresponds to a vertex at a specific simulation frame.

Dataset Structure

The dataset is divided into three splits:

Split	Samples
Train	4,265,580
Validation	376,200
Test	460,920

Features

Feature	Description
`frame`	Simulation frame index
`strand`	Rod/strand identifier
`vertex_id`	Vertex identifier
`pos_x/y/z`	Vertex position
`vel_x/y/z`	Vertex velocity
`force_x/y/z`	Applied forces
`curvature`	Local curvature
`torsion`	Local torsion
`prev_segment_direction`	Previous segment direction vector
`next_segment_direction`	Next segment direction vector
`prev_segment_length`	Previous segment length
`next_segment_length`	Next segment length
`boundary`	Boundary condition information

Data Generation

The dataset was generated using a Discrete Elastic Rods simulation environment with randomized physical parameters and dynamic interactions.

The simulations include temporal evolution of elastic rods under physical constraints and external/internal forces.

Intended Use

This dataset is intended for:

Training Graph Neural Networks
Physics regression tasks
Simulation approximation
Temporal dynamics prediction
Deformable object learning

Limitations

The dataset is fully synthetic.
Results may not perfectly generalize to real-world rod dynamics.
Physical behavior depends on the simulation assumptions and parameter ranges.

License

apache-2.0

Citation

@dataset{der_simulation_dataset,
  title={Discrete Elastic Rods Simulation Dataset},
  author={Samuel Ferreira Santos},
  year={2026}
}

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