My research interests lie in the development of reduced-order models for numerical simulations of complex turbulent reacting flows. To this end, my research has encompassed many aspects of the matter, ranging from the use of model reduction techniques and machine learning approaches such as Principal Component Analysis (PCA), Bayesian methods for regression, to the utilization of advanced CFD methods such as RANS and LES.

My doctorate research project drove me down the path of dimension reduction techniques for large-scale simulation of combustion flows. In particular, my work focused on the use of PCA and nonlinear regression for the development of reduced-order models for LES.

In my current research, I intend to pursue the development of data-driven modeling, especially in LES and DNS, as they are able to reproduce specific characteristics of combustion systems at a fraction of the cost of full order models. 

Belgian Section of the Combustion Institute 

Numerical Combustion Principal Component Analysis Reduced-order models PCA Low-dimensional manifolds