I am first and foremost a Computational Fluid Dynamics (CFD) specialist. To me, CFD extends beyond a black box, and I am particularly engaged in open-source development, especially with OpenFOAM. My doctoral research focused on uncertainty quantification of turbulent flows in turbomachinery, where I developed sparse and efficient methods to assess operational and geometrical uncertainties and applied robust optimization under uncertainty. During my postdoc at Chalmers, I developed numerical methods for transient simulations of hydraulic turbines and expanded into reduced-order modeling, studying approaches such as POD, SPOD, DMD, and sparsity-promoting DMD.
More recently, my research has moved toward integrating machine learning with CFD. I have explored flow control using Deep Reinforcement Learning (DRL) within OpenFOAM, and multi-fidelity physics-informed neural networks for solving PDEs. Altogether, my current line of research lies at the intersection of high-fidelity CFD and data-driven methods, aiming to develop efficient, robust, and trustworthy approaches for the simulation, understanding, and control of complex flows.
Picture below illustrates the scope of my research.
Master’s thesis opportunities:
I supervise master’s thesis projects on topics related to computational and data-driven fluid dynamics. If you are a master’s student at Linköping University looking for a thesis, please see current openings and detailed project descriptions on .
If you already have a project idea related to my research and would like to develop it as a master’s thesis, you are welcome to contact me to discuss feasibility and supervision.
