Publications
- SPIN: A data-driven model to reduce large chemical reaction networks
- Predicting Aggregation Rates of Polycyclic Aromatics Through Machine Learning
- Elucidating the Polycyclic Aromatic Hydrocarbons Involved in Soot Inception
- A Machine Learning Framework to Predict the Aggregation of Polycyclic Aromatic Compounds
- Exploring Soot Inception Rate with Stochastic Modelling and Machine Learning
- Uncertainty-based Weight Determination for Surrogate Optimization
- On the Importance of Species Selection for the Formulation of Fuel Surrogates
- Molecular Structures in Flames: A Comparison Between SNapS2 and Recent AFM Results
- Stochastic and Network Analysis of Polycyclic Aromatic Growth in a Coflow Diffusion Flame
- Oxidation of 2,6-dimethylheptane at Low Temperature: Kinetic Modeling and Experimental Study
- Reaction Pathways for the Formation of Five-membered Rings onto Polyaromatic Hydrocarbon Framework