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
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: [email protected]: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.