CDS&E: Data-driven fast methods for high-energy plasma astrophysics

CDS

• 批准号: 2307684
• 负责人: Dongwook Lee
• 金额: $ 49.03万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2307684&HistoricalAwards=false
• 关键词: CDS&E; Data; driven; fast; methods

Significantly energetic astrophysical flows exhibiting plasma velocities near the speed of light are referred to as relativistic plasmas. These high-energy relativistic flows manifest in a diverse array of astrophysical phenomena involving compact objects. Noteworthy examples encompass core-collapse supernovae, jets and accretion flows surrounding massive compact objects like black holes and neutron stars, pulsar wind nebulae, and gamma-ray bursts. Moreover, astronomical observations consistently indicate the presence of dynamically significant magnetic fields within these highly compressible relativistic flows. Computer simulations serve as indispensable tools for researchers studying these plasmas, facilitating the comprehension of various physical processes associated with immensely energetic relativistic astrophysical flows. Scientists at the University of California, Santa Cruz aim to advance the precision of computer simulations concerning relativistic flows, as current computer algorithms encounter challenges in delivering high-fidelity, dependable numerical solutions. The team will develop a novel data-driven machine-learning strategy that enhances the computational performance and accuracy of numerical solutions for relativistic plasma flows. The expected outcomes of this project will be disseminated to the broader computational astrophysics community through publications in scientific journals and the release of open-source code for improved computer simulations. As part of this project, the PI will also advise and mentor undergraduate students from underrepresented groups via the Cal-Bridge and Lamat REU programs. The project's primary objective is to tackle unresolved challenges in simulating relativistic flows. Currently, simulating relativistic flows using modern shock-capturing schemes necessitates an impractically high grid resolution to achieve grid convergence. To address this issue, the team proposes the development of new data-driven, fast, a-priori shock-capturing methods for relativistic hydrodynamics and magnetohydrodynamics. The proposed approach aims to eliminate the need for computationally expensive conventional "limited reconstruction" of fluid data, which is a nonlinear numerical process required for numerical stability in standard modern shock-capturing methods. To overcome this limitation, the team will create high-order "unlimited" reconstruction algorithms using Gaussian Process (GP) reconstruction. By combining the GP method with a physics-informed artificial neural network, they will introduce a novel data-learned shock-capturing paradigm named the a-priori annMOOD (Artificial Neural Network Multidimensional Optimal Order Detection) method, which will replace the existing a-posteriori procedural shock-capturing MOOD method. The anticipated outcome of this project is a performance-enhanced relativistic (magneto)hydrodynamics (RMHD) solver optimized for massively parallel computing. By leveraging the power of data-driven techniques and high-order reconstruction algorithms, this project aims to significantly improve the efficiency and accuracy of simulating relativistic flows, thereby advancing understanding of these complex phenomena. The resulting solver will be capable of delivering reliable results while reducing the computational burden associated with achieving grid convergence.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.

表现出接近光速的等离子体速度的高能天体物理流被称为相对论等离子体。这些高能相对论流体现在涉及致密天体的各种天体物理现象中。值得注意的例子包括核心塌缩超新星、围绕黑洞和中子星、脉冲星风星云和伽马射线爆发等大质量致密天体的喷流和吸积流。此外，天文观测一致表明这些高度可压缩的相对论流中存在动态显着磁场。计算机模拟是研究这些等离子体的研究人员不可或缺的工具，有助于理解与高能相对论天体物理流相关的各种物理过程。加州大学圣克鲁斯分校的科学家们致力于提高相对论流计算机模拟的精度，因为当前的计算机算法在提供高保真、可靠的数值解决方案方面遇到了挑战。该团队将开发一种新颖的数据驱动机器学习策略，以提高相对论等离子体流数值解的计算性能和准确性。该项目的预期成果将通过科学期刊上的出版物和发布用于改进计算机模拟的开源代码传播给更广泛的计算天体物理学界。作为该项目的一部分，PI 还将通过 Cal-Bridge 和 Lamat REU 项目为代表性不足群体的本科生提供建议和指导。该项目的主要目标是解决模拟相对论流中尚未解决的挑战。目前，使用现代冲击捕获方案模拟相对论流需要不切实际的高网格分辨率才能实现网格收敛。为了解决这个问题，该团队建议为相对论流体动力学和磁流体动力学开发新的数据驱动、快速、先验冲击捕获方法。所提出的方法旨在消除对计算昂贵的传统流体数据“有限重建”的需要，这是标准现代冲击捕获方法中数值稳定性所需的非线性数值过程。为了克服这一限制，该团队将使用高斯过程（GP）重建创建高阶“无限”重建算法。通过将 GP 方法与基于物理的人工神经网络相结合，他们将引入一种新颖的数据学习冲击捕获范式，名为先验 annMOOD（人工神经网络多维最优阶检测）方法，该方法将取代现有的 a-后验程序冲击捕获 MOOD 方法。该项目的预期成果是针对大规模并行计算进行优化的性能增强型相对论（磁）流体动力学 (RMHD) 求解器。通过利用数据驱动技术和高阶重建算法的力量，该项目旨在显着提高模拟相对论流的效率和准确性，从而增进对这些复杂现象的理解。由此产生的求解器将能够提供可靠的结果，同时减少与实现网格收敛相关的计算负担。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和更广泛的影响审查标准进行评估，被认为值得支持。