---
license: apache-2.0
language:
- en
base_model:
- ajsbsd/navier-stokes-2d-dataset
pipeline_tag: graph-ml
tags:
- neural-operator
- fourier-neural-operator
- scientific-machine-learning
- partial-differential-equations
- surrogate-model
datasets:
- ajsbsd/navier-stokes-2d-dataset # Replaced with specific dataset name
metrics:
- l2_error # Example metric
model-index:
- name: Fourier Neural Operator (FNO)
  results:
  - task:
      name: Solving Partial Differential Equations
      type: text-generation # Or a more specific task type if available in Hugging Face tasks
    dataset:
      name: Navier-Stokes 2D Dataset
      type: custom
    metrics:
      - type: l2_error
        value: 0.0 # Replace with actual metric value from training

model_name: "fno_navier_stokes_2d" # More specific name
model_author: "Neural Operator Community/Your Name"
model_summary: "A Fourier Neural Operator (FNO) checkpoint trained on the Navier-Stokes 2D dataset for solving partial differential equations."
# Training Details
training_procedure:
  code_repository: "git@github.com:neuraloperator/NNs-to-NOs.git"
  training_script: "python train_single_res.py fno.yaml"
  epochs: 10
  software_framework: "PyTorch"
  hardware_setup: "Not specified, assumed standard GPU setup (e.g., NVIDIA V100 or A100)"
  training_duration: "Not specified"
  hyperparameters:
    # Example hyperparameters from fno.yaml (please fill with actual values)
    learning_rate: 0.001
    optimizer: "Adam"
    batch_size: 32
    resolution: [64, 64]
    modes: 12
    width: 20
  data_preprocessing: "Refer to the `NNs-to-NOs` repository and `fno.yaml` for data loading and preprocessing details specific to the Navier-Stokes 2D dataset."
  validation_strategy: "Standard validation split as defined in `fno.yaml` or the `train_single_res.py` script."

# Intended Use
intended_uses:
  - "Surrogate modeling for Navier-Stokes 2D equations."
  - "Accelerating scientific simulations of fluid dynamics."
  - "Research and development in neural operators for PDEs."

# Limitations and Biases
limitations:
  - "Performance may degrade on out-of-distribution flow regimes or boundary conditions not present in the training data."
  - "Generalizability is directly tied to the diversity and fidelity of the `ajsbsd/navier-stokes-2d-dataset`."
  - "Scalability to higher-dimensional or more complex fluid dynamics problems needs further evaluation."
biases:
  - "Potential biases inherent in the `ajsbsd/navier-stokes-2d-dataset`, such as specific Reynolds numbers or initial conditions."

# Ethical Considerations
ethical_considerations:
  - "Ensure responsible deployment, especially in applications where simulation accuracy is critical (e.g., engineering design)."
  - "Transparency in the model's limitations and the dataset's characteristics is paramount."
---