Passage of particles through matter and the effective straggling-function: High-fidelity accelerated simulation via Physics-Informed Machine Learning
The paper introduces PHIN-GAN, a physics-informed generative adversarial network that utilizes analytical probability density functions of the Landau straggling function to provide high-fidelity, scalable, and computationally efficient simulations of particle-matter interactions compared to traditional methods like GEANT4.