PTNO: Training Neural Operators with Noisy Monte Carlo Estimates for Particle Transport Problems

Excerpt

arXiv:2609.40090v1 Announce Type: cross Abstract: Particle transport under multiple scattering is central to radiative transfer and plasma physics, yet high-fidelity Monte Carlo (MC) simulations must trace prohibitively many particles. Learning-based surrogates can amortize this cost, but typically train on expensive, well-converged MC solutions. We propose the Particle Transport Neural Operator (PTNO), a neural operator that learns particle transport surrogates directly from noisy, low-cost MC