# CODES: Benchmarking Coupled ODE Surrogates

arXiv: https://arxiv.org/abs/2410.20886 · HTML: https://mlsanity.com/paper/2410.20886/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 11 references that are in the map.
- First posted on arXiv 28 Oct 2024.

## Builds on (most cited first, 5 of 5)

- [Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles](https://mlsanity.com/paper/1612.01474/) (2016)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Neural networks: solving the chemistry of the interstellar medium](https://mlsanity.com/paper/2211.15688/) (2022)
- [Speeding up astrochemical reaction networks with autoencoders and neural ODEs](https://mlsanity.com/paper/2312.06015/) (2023)
- [Emulating the interstellar medium chemistry with neural operators](https://mlsanity.com/paper/2402.12435/) (2024)

## Built on by (most cited first, 1 of 1)

- [NeuralPDR: Neural Differential Equations as surrogate models for Photodissociation Regions](https://mlsanity.com/paper/2506.14270/) (2025)

## Abstract (arXiv)

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.