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

利用深度神经网络训练的代理模型，在加速基于物理的科学模拟方面取得了令人印象深刻的最新进展，这刺激了这个项目，使用这些模型来加速第一批星系形成的宇宙学模拟，包括III星族恒星的化学预富集。该替代模型将在高分辨率自适应网格细化（AMR）模拟中进行训练，并通过将其纳入低空间分辨率的AMR模拟中进行验证。一旦经过验证，该方法将成为Enzo- e的一部分，这是广泛使用的Enzo代码的高度可扩展版本。主要目标是建立这种寻路方法的有效性和实用性，通过将数值模拟与机器学习相结合来解决天文学中的前沿问题。这种技术方法可以为其他天体物理情况定制，在这些情况下，大范围的尺度抑制了直接的数字攻击。它还将有助于促进在多个学科中采用代理模型，并导致改进的科学信息深度学习神经网络设计。替代模型被称为Pop3Net，由几个定制设计的卷积深度神经网络组成，这些网络预测Pop III星形成的时间和地点，以及其他模拟辐射和化学反馈效应的网络。Pop3Net将使用最先进的第一个星系形成的全物理模拟进行训练。混合的Enzo-E+Pop3Net代码将用于模拟高红移星系的形成，使用Powderday包计算合成观测结果，与詹姆斯韦伯太空望远镜和南希格蕾丝罗马太空望远镜的未来数据进行比较。这部分工作将检验星系特性对第一批星系的化学富集历史有多敏感，包括超音速重子-暗物质流速度效应。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。

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.