NeuralOperator — Detailed Introduction
NeuralOperator is a comprehensive PyTorch library for learning neural operators, with official implementations of Fourier Neural Operators (FNO) and related operator-learning architectures. Neural operators learn mappings between function spaces (e.g., inputs and solutions of PDEs) rather than mappings between finite-dimensional vectors; this enables models trained at one spatial/temporal resolution to generalize to different resolutions (resolution invariance).
Key features
- Official implementations of Fourier Neural Operators (FNO) and other operator architectures.
- Resolution-invariant operator learning: trained operators can be applied at different grid resolutions without retraining.
- Tensorization support (e.g., Tucker factorization) to reduce parameter count and improve efficiency (TFNO variants).
- PyTorch ecosystem integration and examples, ready-to-run training scripts and demo problems.
- Documentation site with guides and practical notes, and recommended citation entries for academic use.
Quickstart (example)
from neuralop.models import FNO
operator = FNO(n_modes=(64, 64),
hidden_channels=64,
in_channels=2,
out_channels=1)Tensorized variant example (Tucker TFNO):
from neuralop.models import TFNO
operator = TFNO(n_modes=(64, 64),
hidden_channels=64,
in_channels=2,
out_channels=1,
factorization='tucker',
implementation='factorized',
rank=0.1)Installation
-
Clone and install in editable mode:
git clone https://github.com/NeuralOperator/neuraloperator cd neuraloperator pip install -e . pip install -r requirements.txt
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Or install the stable release from PyPI:
pip install neuraloperator
Who is it for
NeuralOperator is aimed at researchers and engineers working on scientific machine learning, numerical simulation, and physics-informed ML who need models that generalize across discretizations. It is suitable for experimenting with operator-learning architectures, benchmarking against PDE problems, and integrating efficient tensorized variants for large-scale problems.
Citations and further reading
The repository provides recommended citation entries (including the 2023 JMLR neural operator paper and subsequent library/guides) and links to practical guides and documentation for deeper understanding and reproducible experiments.
