Collaborative Research: CDS&E: AI-Enhanced Exascale Simulations Of The Earliest Galaxies

合作研究：CDS

• 批准号: 2108076
• 负责人: Michael Norman
• 金额: $ 46.35万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2108076&HistoricalAwards=false
• 关键词: Collaborative; Research; CDS& AI; Enhanced

Impressive recent advances in accelerating physics-based scientific simulations using deep neural network-trained surrogate models have stimulated this project, to use such models to accelerate cosmological simulations of the formation of the first galaxies, including chemical pre-enrichment by Population III stars. The surrogate model will be trained on high-resolution adaptive mesh refinement (AMR) simulations and validated by incorporating it into AMR simulations of lower spatial resolution. Once validated, this approach will become part of Enzo-E, the highly scalable version of the widely used Enzo code. A principal goal is to establish the validity and usefulness of this pathfinding approach, addressing a frontier problem in astronomy by combining numerical simulations with machine learning. This technical methodology can be customized for other astrophysical situations where a vast range of scales inhibits a direct numerical assault. It will also help to stimulate adoption of surrogate models in multiple disciplines, and lead to improved science-informed deep learning neural network designs.The surrogate model, to be called Pop3Net, is composed of several convolutional deep neural networks of custom design which predict when and where Pop III stars form, and other networks that emulate radiative and chemical feedback effects. Pop3Net will be trained using state-of-the-art full-physics simulations of the formation of the first galaxies. The hybrid Enzo-E+Pop3Net code will be used to simulate the formation of large ensembles of high redshift galaxies, computing synthetic observations using the Powderday package, to compare with future data from the James Webb Space Telescope and the Nancy Grace Roman Space Telescope. This part of the work will examine how sensitive galaxy properties are to the chemical enrichment history of the first galaxies, including supersonic baryon-dark matter streaming velocity effects.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.

在使用深度神经网络训练的替代模型加速基于物理学的科学模拟方面的最新进展激发了该项目，以使用此类模型来加速宇宙学模拟第一个星系的形成，包括人群III恒星的化学预耐用。 替代模型将在高分辨率自适应网格改进（AMR）模拟中进行培训，并通过将其纳入较低空间分辨率的AMR模拟来验证。 一旦验证，此方法将成为Enzo-E的一部分，Enzo-E是广泛使用的ENZO代码的高度可扩展版本。 一个主要目标是建立这种探索方法的有效性和实用性，并通过将数值模拟与机器学习结合在一起来解决天文学的边界问题。 可以针对其他大量尺度抑制直接数值攻击的其他天体物理情况来定制这种技术方法。 它还将有助于刺激多个学科中的替代模型的采用，并导致改进的科学知识深度学习神经网络设计。替代模型，称为POP3NET，由几种卷积的卷积深神经网络组成，这些卷积深度神经网络由何时何时形成POP III恒星，以及模拟辐射和化学反馈作用的其他网络。 POP3NET将使用最新的完整物理模拟进行培训。 混合ENZO-E+POP3NET代码将用于模拟高红移星系的大型组合的形成，使用粉末日软件包计算合成观察结果，以与James Webb Space望远镜和Nancy Grace Roman Roman Space望远镜的未来数据进行比较。 这部分工作将研究对第一星系的化学富集历史的敏感星系特性，包括超音速Baryon-dark物质流速度效应。该奖项反映了NSF的法定任务，并被认为值得通过基金会的知识分子和更广泛的影响来通过评估来通过评估来获得支持。

项目成果

Connecting Primordial Star-forming Regions and Second-generation Star Formation in the Phoenix Simulations

连接凤凰模拟中的原始恒星形成区域和第二代恒星形成

• DOI: 10.3847/1538-4357/ac6c87
• 发表时间: 2022
• 期刊: The Astrophysical Journal
• 作者: Wells, Azton I.;Norman, Michael L.
• 通讯作者: Norman, Michael L.