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A predictive physics-aware hybrid reduced order model for reacting flows
The number of degrees of freedom is reduced from thousands of temporal points to a few POD modes with their corresponding temporal coefficients.
Prediction of liquid fuel properties using machine learning models with Gaussian processes and probabilistic conditional generative learning
Accurate determination of fuel properties of complex mixtures over a wide range of pressure and temperature conditions is essential to utilizing alternative fuels.
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