Model: ajsbsd/navier-stokes-2d-dataset
Author: ajsbsd
Pipeline: graph-ml
Tags: neural-operator, fourier-neural-operator, scientific-machine-learning, partial-differential-equations, surrogate-model, graph-ml, en, dataset:ajsbsd/navier-stokes-2d-dataset, base_model:ajsbsd/navier-stokes-2d-dataset, base_model:finetune:ajsbsd/navier-stokes-2d-dataset, license:apache-2.0, model-index, region:us
Base models (finetune): id: ajsbsd/navier-stokes-2d-dataset
Card metadata: 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
metrics: l2_error
model-index: name: Fourier Neural Operator (FNO)
results: task: name: Solving Partial Differential Equations
type: text-generation
dataset: name: Navier-Stokes 2D Dataset
type: custom
metrics: type: l2_error
value: 0
verified: no
model_name: fno_navier_stokes_2d
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_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: 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_uses: Surrogate modeling for Navier-Stokes 2D equations., Accelerating scientific simulations of fluid dynamics., Research and development in neural operators for PDEs.
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: 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.
citation: @article{Berner2025PrincipledAF,
  title={Principled Approaches for Extending Neural Architectures to Function Spaces for Operator Learning},
  author={Julius Berner and Miguel Liu-Schiaffini and Jean Kossaifi and Valentin Duruisseaux and Boris Bonev and Kamyar Azizzadenesheli and Anima Anandkumar},
  journal={arXiv:2506.10973},
  year={2025},
  url={https://arxiv.org/abs/2506.10973}
}

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Created: 2025-06-14 18:19:47