CODES为耦合微分方程代理模型提供多维度评估基准
CODES: Benchmarking Coupled ODE Surrogates
- 构建涵盖插值、外推等六维度的综合评测框架
- 支持并行训练与配置生成，降低使用门槛
- 适合需可靠代理模型的科研人员和工程应用
我们提出CODES，一个用于全面评估耦合常微分方程（ODE）系统代理架构的基准。除标准指标如均方误差（MSE）和推理时间外，CODES还从插值、外推、稀疏数据、不确定性量化及梯度相关性等多个维度揭示代理模型的行为特性。该基准通过集成并行训练、基于网页的配置生成器以及预实现的基线模型和数据集，强调易用性。详尽文档保障可持续性，并为协作改进奠定基础。通过提供公平且多角度的对比，CODES帮助研究者根据特定数据集和应用场景选择最合适的代理模型，同时深化对代理学习行为的理解。
原文摘要 · Abstract (English)
We introduce CODES, a benchmark for comprehensive evaluation of surrogate architectures for coupled ODE systems. Besides standard metrics like mean squared error (MSE) and inference time, CODES provides insights into surrogate behaviour across multiple dimensions like interpolation, extrapolation, sparse data, uncertainty quantification and gradient correlation. The benchmark emphasizes usability through features such as integrated parallel training, a web-based configuration generator, and pre-implemented baseline models and datasets. Extensive documentation ensures sustainability and provides the foundation for collaborative improvement. By offering a fair and multi-faceted comparison, CODES helps researchers select the most suitable surrogate for their specific dataset and application while deepening our understanding of surrogate learning behaviour.
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