arXiv:2607. 04450v1 Announce Type: cross Abstract: Integral codes like the Accident Source Term Evaluation Code (ASTEC) are powerful tools to study the physics of Severe Accidents (SAs) in nuclear reactors.
By Alessandro Longhi, Danny Lathouwers, Zolt\'an Perk\'o
arXiv:2601.12971v2 Announce Type: replace
Abstract: Physics-informed neural networks (PINNs) can be limited by coordinate representations and conflicting gradients from heterogeneous physical constra...
By Pancheng Niu, Jun Guo, Qiaolin He, Yongming Chen, Yanchao Shi
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.
By Yubo Cao, Xi Deng, Mengqi Xia, Vignesh Gopakumar, Ander Gray, Anima Anandkumar
arXiv:2609.22329v1 Announce Type: cross
Abstract: Black-Box Optimization (BBO) is often applied in several engineering fields and can utilize an advancement of numerical measure- ments and simulation...
By Md Khadimul Islam Zim (Czech Academy of Sciences, Institute of Computer Science, Prague, Czech Republic), Martin Hole\v{n}a (Czech Academy of Sciences, Institute of Computer Science, Prague, Czech Republic)
arXiv:2609.20868v1 Announce Type: cross
Abstract: Transfer and multi-nucleon transfer reactions are essential tools for probing nuclear structure and reaction dynamics, requiring precise determinatio...
By M. Rejmund, A. Lemasson, P. Morfouace, D. Ramos, J. Taieb, J. D. Frankland
KATOsuper is an objective‑agnostic framework that accelerates neural topology optimization by coupling neural‑reparameterized TO with a Sensitivity‑Consistent Fourier Neural Operator (SC‑FNO). It uses a forward_split architecture to ensure that sensitivities derived via automatic differentiation remain consistent with predicted objectives, enabling stable optimization. The method demonstrates significant deployment‑time speedups (15–110×) over MATLAB baselines while preserving optimality across 2D and 3D benchmark problems, including compliance and stress minimization, and supports zero‑shot extrapolation to higher resolutions.
By Shengyu Yan, Jasmin Jelovica