Efficient Neural Surrogates for Linear Radiation Transport on the Lattice and Hohlraum benchmarks

Excerpt

arXiv:2610.04665v1 Announce Type: new Abstract: Linear radiation transport equations (RTEs) form the simulation foundations underpinning design and analysis tasks in nuclear engineering, inertial confinement fusion, medical imaging, and astrophysics, but resolving the high-dimensional phase space at engineering fidelity remains expensive enough that outer-loop workflows, such as design optimization, uncertainty quantification, and parameter sweeps, are routinely budget-bound on traditional solve