发表机构
Vanderbilt University; Los Alamos National Laboratory（范德堡大学; 洛斯阿拉莫斯国家实验室）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
ASTRAL框架通过主动采样和回归构建替代模型，实现恒星演化模拟参数空间的最优布局与插值预测，显著优于均匀布局，并可扩展至其他高维昂贵模拟领域。
AI 中文摘要
生成密集的详细恒星演化模拟网格在计算上是不可行的，因为解析日益复杂的核合成链和动力学时标会带来迅速增加的计算成本，该成本随维度和分辨率而扩展。现有的模拟网格稀疏、异构，且往往覆盖参数空间的不同区域，限制了（直接的）系统比较和向下游科学分析的更广泛泛化。我们提出ASTRAL（\textbf{A}ctive \textbf{S}imulation \textbf{T}uning and \textbf{R}egression for \textbf{A}strophysical \textbf{L}ibraries，天体物理库主动模拟调优与回归），一个公开可用的恒星演化模型最优布局与仿真框架。ASTRAL框架构建替代模型，提供由稀疏模拟网格填充的参数空间的连续表示。此外，ASTRAL在模拟参数空间（如零年龄主序质量）上进行插值，以预测未显式模拟系统的恒星属性，并附带相关不确定性估计。我们表明，ASTRAL在构建既能捕捉输出特征全局和局部变异性的仿真就绪模型库方面，显著优于均匀布局。我们在一个简单的一维MESA模型背景下展示了我们框架的性能。最后，我们通过分析我们的仿真器在应用于超新星模拟时的预测置信度，检验了我们的方法如何为下游应用提供信息。我们强调，虽然ASTRAL是为恒星演化开发的，但它可直接扩展到其他模拟驱动领域，在这些领域中模型评估昂贵且参数空间是高维的。
英文摘要
Generating dense grids of detailed stellar evolution simulations is computationally prohibitive, as resolving progressively complex nucleosynthesis chains and dynamical timescales incurs a rapidly increasing computational cost that scales with dimensionality and resolution. Existing simulation grids are sparse, heterogeneous, and often cover disparate regions of parameter space, limiting (direct) systematic comparison and broader generalization to downstream science analyses. We present ASTRAL (\textbf{A}ctive \textbf{S}imulation \textbf{T}uning and \textbf{R}egression for \textbf{A}strophysical \textbf{L}ibraries), a publicly available framework for optimal placement and emulation of stellar evolution models. The ASTRAL framework builds surrogate models that provide a continuous representation of the parameter space which is populated by sparse simulation grids. Furthermore, ASTRAL interpolates across the simulation parameter space (e.g. zero-age-main-sequence mass) to predict stellar properties of stars with associated uncertainty estimates for systems not explicitly simulated. We show that ASTRAL significantly outperforms uniform placement in constructing emulation-ready model libraries that capture both global and local variability in the output features. We showcase the performance of our framework in the context of a simple, one-dimensional MESA model. Finally, we examine how our methodology can inform downstream applications by analyzing our emulator's predictive confidence when applied to supernova simulations. We emphasize that, while developed for stellar evolution, the ASTRAL framework is directly extensible to other simulation-driven domains where model evaluations are expensive and parameter spaces are high-dimensional.
Commentsaccepted to PRD
