物理约束神经算子高速求解旋转相对论性恒星结构，实现 10³ 倍加速的无监督方程推断
Unsupervised Neural Operators for Rotating Relativistic Stars: High-Fidelity Equation of State Inference and Pulsar Spin Evolution at Scale
AI 导读
研究者开发出物理约束的神经算子，用于求解控制旋转中子星内部的轴对称广义相对论流体静力学平衡偏微分方程。求解器完全无监督训练，训练目标仅由偏微分方程驱动，在精度几乎无损的情况下实现 10³ 倍加速。该方法使自洽考虑自转的物态方程推断和双星演化中的重复恒星结构计算变得可扩展，让包含自转结构与物态方程不确定性的脉冲星自旋预测可被种群合成研究使用。
正文 · 原文
Abstract:Observed populations of rotating neutron stars~(NSs) with millisecond-order periods are powerful probes of fundamental physics. Mass-radius measurements inform poorly constrained properties of degenerate nuclear matter, whereas high-fidelity population synthesis connects unknowns in NS formation and binary evolution to large radio-timing surveys. However, repeated structure calculations are prohibitive due to the computational cost of solving the highly coupled partial differential equations of axisymmetric general relativistic hydrostatic equilibrium that govern rotating NS interiors. Non-rotating calculations or approximate rotational corrections can bias equation of state inference and limit the fidelity of population synthesis in tracing the evolutionary history of progenitors and propagating equation of state uncertainties. In this \textit{Letter}, we develop physics-informed neural operators that can solve the axisymmetric problem with high accuracy and negligible computational cost. \textit{Our solvers are trained in a fully unsupervised setting, with the training objective informed solely by the partial differential equations, and achieve a $10^3\times$ speed-up with negligible accuracy loss}. These solvers enable scalable EoS inference that self-consistently accounts for rotation and repeated stellar-structure calculations during binary evolution, making spin predictions that account for rotational structure and EoS uncertainties computationally accessible to population synthesis.
| Subjects: | High Energy Astrophysical Phenomena (astro-ph.HE); Artificial Intelligence (cs.AI); Machine Learning (cs.LG); General Relativity and Quantum Cosmology (gr-qc) |
| Cite as: | arXiv:2611.05790 [astro-ph.HE] |
| (or arXiv:2611.05790v1 [astro-ph.HE] for this version) | |
| https://doi.org/10.48550/arXiv.2611.05790 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Anarya Ray [view email]
[v1]
Wed, 4 Nov 2026 00:57:22 UTC (1,484 KB)
来源：arXiv cs.AI · arxiv.org