Data-driven inverse design framework using surrogate modeling and genetic algorithms to optimize aviation fuel blends for reduced pollutant emissions under property and composition constraints.
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The Fuel Optimizer: A Data-Driven Numerical Framework for Formulation of Aviation Turbine Fuel
Data-driven inverse design framework using surrogate modeling and genetic algorithms to optimize aviation fuel blends for reduced pollutant emissions under property and composition constraints.