A Library for Learning Neural Operators
This provides a practical tool for researchers and practitioners in scientific computing and machine learning to apply operator learning methods, though it is incremental as it packages existing concepts into a library.
The authors introduced NeuralOperator, an open-source Python library for learning neural operators, which generalize neural networks to map between function spaces rather than finite-dimensional Euclidean spaces, enabling training and inference on functions at various discretizations with convergence properties.
We present NeuralOperator, an open-source Python library for operator learning. Neural operators generalize neural networks to maps between function spaces instead of finite-dimensional Euclidean spaces. They can be trained and inferenced on input and output functions given at various discretizations, satisfying a discretization convergence properties. Built on top of PyTorch, NeuralOperator provides all the tools for training and deploying neural operator models, as well as developing new ones, in a high-quality, tested, open-source package. It combines cutting-edge models and customizability with a gentle learning curve and simple user interface for newcomers.