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A Data-driven Approach to Multiscale Methods for ScalableTransport in Neutron Star Mergers and Complex Plasmas

中子星合并和复杂等离子体中可扩展传输的数据驱动多尺度方法

基本信息

批准号：
2008004
负责人：
Andrew Christlieb
金额：
$ 43.34万
依托单位：
Michigan State University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2008004&HistoricalAwards=false
关键词：
Data driven Approach Multiscale Methods

项目摘要

The detection of multimessenger astrophysical signals is one of the most exciting and significant new areas in physics. However, extracting meaningful physical understanding from observations depends fundamentally on accurately modeling and simulating such complex multiphysics, multiscale systems. For example, neutrinos play a key role in neutron star mergers, but the extreme complexity and scale of this class of problem force the use of severe, and often inadequate, approximations. This project directly addresses these deficiencies with a novel approach, which will be open-source and incorporated into the widely-used FLASH radiation hydrodynamics code, thus benefiting the broader computational astrophysics community. The research includes significant mentoring of graduate students and postdoctoral researchers, who will develop skills that are in high demand.The work will advance the state-of-the-art in modeling neutrino transport in high-accuracy simulations of binary neutron star mergers and core-collapse supernovae, developing surrogate models for the kinetic equations by using machine learning (ML) and reduced order modeling (ROM), to achieve scalable multi-scale transport simulations. The framework, dubbed Surrogate Transport Adaptive Model Procedure (STAMP), replaces algebraic closure with adaptive computed methods from a surrogate model, and retains the highly-scalable nature of moment methods while recovering the accuracy of multi-angle approaches. This project advances the goals of the NSF Windows on the Universe Big Idea.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

多信使天体物理信号的探测是物理学中最令人兴奋和重要的新领域之一。然而，从观测中提取有意义的物理理解从根本上取决于准确地建模和模拟这种复杂的多物理场、多尺度系统。例如，中微子在中子星合并中起着关键作用，但这类问题的极端复杂性和规模迫使我们使用严格的、往往不充分的近似。这个项目用一种新颖的方法直接解决了这些缺陷，该方法将是开源的，并被纳入广泛使用的FLASH辐射流体动力学代码中，从而使更广泛的计算天体物理学社区受益。这项研究包括对研究生和博士后研究人员的重要指导，他们将培养出高需求的技能。这项工作将在双中子星合并和核心坍缩超新星的高精度模拟中推进最先进的中微子输运建模，通过使用机器学习（ML）和降阶建模（ROM）开发动力学方程的替代模型，以实现可扩展的多尺度输运模拟。该框架被称为代理传输自适应模型过程（STAMP），它用代理模型中的自适应计算方法取代了代数闭包，并在恢复多角度方法精度的同时保留了矩方法的高度可扩展性。这个项目推进了NSF宇宙大构想窗口的目标。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。