# Entropy-Constrained Machine Learning with Residual Data Augmentation for Modeling Chemical Kinetics

Source: [Entropy-Constrained Machine Learning with Residual Data Augmentation for Modeling Chemical Kinetics](<https://www.sota2.com/research/paper/6a54715232fb9373148770f4>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2026-07-14T05:00:06.675Z

Authors: Okezzi Ukorigho, Opeoluwa Owoyele

Published: 2026-07-10T16:31:40.000Z

## Abstract

We present a physics-constrained machine learning framework for accelerating the direct numerical simulation (DNS) of turbulent reacting flows. The model replaces the direct evaluation of detailed chemical source terms with a surrogate that predicts reaction rates from a reduced thermochemical state. To improve physical consistency, the second law of thermodynamics is incorporated as a training constraint by enforcing non-negative entropy generation, which restricts the evolution of the thermochemical state to physically admissible directions and improves stability during time integration. The approach is demonstrated on DNS of a two-dimensional planar lean premixed methane-air flame interacting with a turbulent flow field. The model reproduces detailed-chemistry results with high fidelity while achieving more than an order-of-magnitude reduction in computational cost. Furthermore, a residual-based synthetic data augmentation strategy enables parametric exploration by constructing new training data from the original dataset, allowing accurate simulation at new inlet conditions without additional detailed-chemistry CFD runs. These results demonstrate that thermodynamically constrained machine learning can provide reliable and computationally efficient surrogates for detailed chemistry in high-fidelity combustion simulations.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2607.09582>)

[PDF](<https://arxiv.org/pdf/2607.09582.pdf>)