HyperNOs 是一个自动化并行的神经算子研究库，助力高效探索模型超参数。
HyperNOs: Automated and Parallel Library for Neural Operators Research
- 基于 Ray-tune 实现超参数自动优化与并行计算
- 支持多数据集、多分辨率训练，集成 FNO 与 CNO 模型
- 界面友好且可扩展，适合快速实验与新架构开发
本文提出 HyperNOs，一个基于 PyTorch 的神经算子研究自动化并行库，重点实现超参数空间的全面高效探索。该库利用 Ray-tune 中先进的优化算法与并行计算能力，显著提升神经算子的调参效率。提供用户友好的功能接口，支持固定参数量训练、多数据集及不同分辨率下的模型训练。集成傅里叶神经算子（Fourier Neural Operators, FNO）与卷积神经算子（Convolutional Neural Operators, CNO），在多个代表性基准上达到当前最优性能，验证其处理真实数据与现代架构的能力。库设计简洁易用，配备预置模型与数据集，同时支持新数据集与自定义神经算子架构的快速扩展。
原文摘要 · Abstract (English)
This paper introduces HyperNOs, a PyTorch library designed to streamline and automate the process of exploring neural operators, with a special focus on hyperparameter optimization for comprehensive and exhaustive exploration. Indeed, HyperNOs takes advantage of state-of-the-art optimization algorithms and parallel computing implemented in the Ray-tune library to efficiently explore the hyperparameter space of neural operators. We also implement many useful functionalities for studying neural operators with a user-friendly interface, such as the possibility to train the model with a fixed number of parameters or to train the model with multiple datasets and different resolutions. We integrate Fourier neural operators and convolutional neural operators in our library, achieving state of the art results on many representative benchmarks, demonstrating the capabilities of HyperNOs to handle real datasets and modern architectures. The library is designed to be easy to use with the provided model and datasets, but also to be easily extended to use new datasets and custom neural operator architectures.
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