In this structural matter seminar, Dr. Alessandro Lucantonio presents his research on advancing symbolic regression for model discovery, with a focus on developing new algorithms to uncover interpretable and accurate models for complex physical systems. Key areas of exploration include reduced-order modeling for fluid mechanics, robotic control, and fracture mechanics.
As many real-world aerosols present measurements challenges due to their complex morphology and composition, new insights can be gained through characterizing aerosol particles by more than one dimension. This seminar discusses these bidimensional distributions, the mathematical operations required to realize them, and the discoveries that these distributions can unlock.
Seminar – Dr. Alessandro Lucantonio: Physics-Based Model Discovery with Symbolic Regression 
Seminar: Dr. Tim Sipkens – Bidimensional distributions: enabling the next generation of aerosol characterization