CODES: Benchmarking Coupled ODE Surrogates
This work addresses the need for standardized evaluation in computational science and engineering, though it is incremental as it builds on existing benchmarking practices.
The authors tackled the problem of evaluating surrogate architectures for coupled ODE systems by introducing CODES, a benchmark that provides comprehensive metrics and insights, resulting in a tool that helps researchers select suitable surrogates and understand learning behavior.
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.