开源神经算子库，让模型直接处理函数间映射。
A Library for Learning Neural Operators
- 用神经算子统一建模函数空间映射，支持不同离散化输入输出。
- 兼容PyTorch生态，提供训练、部署与新模型开发全套工具。
- 兼顾前沿性能与易用性，适合初学者和研究者快速上手。
我们提出NeuralOperator——一个用于算子学习的开源Python库。神经算子将神经网络推广到函数空间之间的映射，而非有限维欧氏空间。它们可在不同离散化条件下对输入和输出函数进行训练与推理，并满足离散化收敛性。作为官方PyTorch生态系统的一部分，NeuralOperator提供了高质量、经过测试的完整工具链，支持神经算子模型的训练、部署与新模型开发。该库结合了前沿模型与高度可定制性，同时具备友好的学习曲线和简洁的用户界面，便于新手快速入门。
原文摘要 · Abstract (English)
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. Part of the official PyTorch Ecosystem, 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.
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