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The JWT signature verification failed. Check the signing key and the algorithm.
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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Out-Of-Distribution Evaluation Set For The 2D Gray-Scott Reaction-Diffusion Equation

Out-of-distribution evaluation set for the 2d gray-scott reaction-diffusion equation. The underlying PDE is the Gray-Scott reaction-diffusion.

This dataset contains out-of-distribution (OOD) samples for evaluating model generalization.

Data is stored in HDF5 (.h5) format.

Origin

This dataset was generated by Armand Kassai Koupaï and has been used in the following papers:

  • ZEBRA: In-Context Generative Pretraining for Solving Parametric PDEs — Louis Serrano, Armand Kassaï Koupaï, Thomas X Wang, Pierre Erbacher, Patrick Gallinari. ICML 2025. OpenReview
  • ENMA: Tokenwise Autoregression for Generative Neural PDE Operators — Armand Kassaï Koupaï, Lise Le Boudec, Louis Serrano, Patrick Gallinari. NeurIPS 2025.

Download

See the full download script in the Zebra repository.

pip install huggingface_hub

python download_data/download_data_hugging_face.py --datasets gs_ood

Usage

import h5py

data = h5py.File("train.h5", "r")
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