My main research interest is the development of Physics-Informed Machine Learning frameworks and their application to several fields, such as Radiative Transfer, Rarefied-gas Dynamics, Nuclear Reactor Dynamics, Chemical Kinetics, Epidemiology, Systems Biology, and Spacecraft Orbit Determination. Below you can find some of my works.
Extreme Theory of Functional Connections
A physics-informed neural network method for solving parametric differential equations.
Radiative Transfer
Solutions of Chandrasekhar basic problem in radiative transfer via theory of functional connections.
Rarefied-Gas Dynamics
Physics-informed neural networks for rarefied-gas dynamics: Poiseuille, Couette, and thermal creep flows in the BGK approximation.
