Entropy-Constrained Machine Learning with Residual Data Augmentation for Modeling Chemical Kinetics
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
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- PossiblePossibly related (embedding) · 57%SciML/NeuralPDE.jl →
- PossiblePossibly related (embedding) · 48%neonwatty/machine-learning-refined →
- PossiblePossibly related (embedding) · 47%NVIDIA/physicsnemo →
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- LinkedLinked via arxiv author · 85%Okezzi Ukorigho →
“Entropy-Constrained Machine Learning with Residual Data Augmentation for Modeling Chemical Kinetics”
- LinkedLinked via arxiv author · 85%Opeoluwa Owoyele →
“Entropy-Constrained Machine Learning with Residual Data Augmentation for Modeling Chemical Kinetics”
- FuzzySimilar title/name (fuzzy) · 66%stefan-jansen/machine-learning-for-trading →
“Fuzzy title match (0.78): “Entropy-Constrained Machine Learning with Residual Data Augm” ≈ “stefan-jansen/machine-learning-for-trading””