Artificial Intelligence and Data Driven Optimization of Internal Combustion Engines

1. Active-learning for fuel optimization 2. High throughput screening for fuel formulation 3. Engine optimization using computational fluid dynamics-Genetic algorithms (CFD-GA) 4. Engine optimization using computational fluid dynamics-design of experiments (CFD-DoE) 5. Engine optimization using machine learning-genetic algorithms (ML-GA) 6. Machine learning driven sequential optimization using dynamic exploration and exploitation 7. Optimization of after-treatment systems using machine learning 8. Engine cycle-to-cycle variation control 9. Prediction of low pressure preignition using machine learning 10. AI aided optimization of experimental engine calibration 11. AI aided optimization of vehicle control calibration

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• Tecnologia automotiva e comércios

Detailed fuel chemistry; Active learning; Adaptive learning; Artificial neural networks; Automated machine learning; CFD; Combustion; Computational fluid dynamics; Cycle-to-cycle variability; Cyclic variability; Design of experiments; Design optimization; Dilute SI combustion; EGR; Engine combustion system; Engine optimization; Engine simulation; Engines; Ensemble machine learning; Exhaust gas recirculation; Fuel design; Gaussian process; Genetic algorithm; Genetic algorithms; Hyperparameter optimization; Internal combustion engine; Internal combustion engines; Kinetics; Learning reference governor; Machine learning; Mixed-mode combustion; Neural network surrogate model; Neural networks; Optimization; Preignition; Spiking neural networks; Stochastic optimal control; Super knock