# ChemNODE: A Neural Ordinary Differential Equations Approach for Chemical Kinetics Solvers

arXiv: https://arxiv.org/abs/2101.04749 · HTML: https://mlsanity.com/paper/2101.04749/

## Key facts

- Cited by 26 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2020.

## Built on by (most cited first, 26 of 26)

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- [A Posteriori Evaluation of a Physics-Constrained Neural Ordinary Differential Equations Approach Coupled with CFD Solver for Modeling Stiff Chemical Kinetics](https://mlsanity.com/paper/2312.00038/) (2023)
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- [FNODE: Flow-Matching for data-driven simulation of constrained multibody systems](https://mlsanity.com/paper/2509.00183/) (2025)
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- [Autonomous Adaptive Solver Selection for Chemistry Integration via Reinforcement Learning](https://mlsanity.com/paper/2604.00264/) (2026)
- [Model synthesis and identifiability analysis of stiff chemical reaction systems with inVAErt networks](https://mlsanity.com/paper/2605.04134/) (2026)
- [Entropy-Constrained Machine Learning with Residual Data Augmentation for Modeling Chemical Kinetics](https://mlsanity.com/paper/2607.09582/) (2026)
- [A Physics-Chemistry-Informed Neural Network (PCINN) for Real-Time Spatial-ALD Coverage Prediction and Reliable Kinetics Inversion](https://mlsanity.com/paper/2608.00212/) (2026)
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## Abstract (arXiv)

Solving for detailed chemical kinetics remains one of the major bottlenecks for computational fluid dynamics simulations of reacting flows using a finite-rate-chemistry approach. This has motivated the use of fully connected artificial neural networks to predict stiff chemical source terms as functions of the thermochemical state of the combustion system. However, due to the nonlinearities and multi-scale nature of combustion, the predicted solution often diverges from the true solution when these deep learning models are coupled with a computational fluid dynamics solver. This is because these approaches minimize the error during training without guaranteeing successful integration with ordinary differential equation solvers. In the present work, a novel neural ordinary differential equations approach to modeling chemical kinetics, termed as ChemNODE, is developed. In this deep learning framework, the chemical source terms predicted by the neural networks are integrated during training, and by computing the required derivatives, the neural network weights are adjusted accordingly to minimize the difference between the predicted and ground-truth solution. A proof-of-concept study is performed with ChemNODE for homogeneous autoignition of hydrogen-air mixture over a range of composition and thermodynamic conditions. It is shown that ChemNODE accurately captures the correct physical behavior and reproduces the results obtained using the full chemical kinetic mechanism at a fraction of the computational cost.