arXiv:2607. 19388v1 Announce Type: new Abstract: This work extends our one-dimensional single-sweep neural-operator studies to two dimensions.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.
The paper introduces PTNO, a neural operator that learns particle transport surrogates directly from noisy, low‑cost Monte Carlo (MC) labels, addressing high variance and high dynamic range challenges. By training on many noisy scenes, PTNO achieves comparable accuracy to converged MC while dramatically reducing computational cost, and it employs a softplus output and a pointwise relative L2 loss to handle HDR data. Experiments on neutron transport in fusion reactors and radiative transfer in participating media show speedups of up to 10⁵× and cost reductions of up to 10⁵× compared to traditional MC.
Efficient determination of the effective multiplication factor (keff) is an important computational task in reactor core neutronics analysis. Physics-informed neural networks (PINNs) incorporate neutr...
The paper introduces JI-PINN, a joint initialization strategy for physics-informed neural networks that simultaneously initializes neutron flux and the effective multiplication factor (keff) using a low-resolution approximate solution. By jointly optimizing both quantities under physical constraints, the method achieves significant reductions in computational time—up to nearly 50%—across various benchmark neutron diffusion problems while maintaining accuracy. It also reduces anomalous keff deviations, offering a more robust approach to solving K‑eigenvalue problems with PINNs.
arXiv:2606. 05202v1 Announce Type: cross Abstract: In reactor physics, neutronics can be treated with different fidelity levels, according to the needs of the user.
The paper presents a continually learning neural‑operator surrogate for the three‑dimensional forward operator used in time‑domain airborne electromagnetic (AEM) Bayesian inversion. By training on successive geological priors and employing an ensemble‑disagreement validity check, the surrogate replaces the expensive forward solver, enabling the Markov chain Monte Carlo sampler to reproduce the full‑solver posterior with credible intervals within 2.6 % of the truth. Applied to the 2013 Capricorn TEMPEST survey, the surrogate inverts over two million soundings in seconds, making uncertainty‑quantified conductivity imaging at survey scale feasible for near real‑time mineral‑systems targeting.
arXiv:2606. 04033v1 Announce Type: new Abstract: The validation of advanced nuclear reactor designs and fuel concepts requires critical experiments with high neutronic similarity to the target technology.