CODES: Benchmarking Coupled ODE Surrogates

Robin Janssen, Immanuel Sulzer, Tobias Buck

Abstract:

We introduce CODES, a benchmark for comprehensive evaluation of surrogate ar-
chitectures 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, uncer-
tainty 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 doc-
umentation ensures sustainability and provides the foundation for collaborative
improvement. By offering a fair and multi-faceted comparison, CODES helps re-
searchers select the most suitable surrogate for their specific dataset and application
while deepening our understanding of surrogate learning behaviour.