Physics-informed neural networks for modeling astrophysical shocks

Author:

Moschou S PORCID,Hicks EORCID,Parekh R Y,Mathew D,Majumdar S,Vlahakis NORCID

Abstract

Abstract Physics-informed neural networks (PINNs) are machine learning models that integrate data-based learning with partial differential equations (PDEs). In this work, for the first time we extend PINNs to model the numerically challenging case of astrophysical shock waves in the presence of a stellar gravitational field. Notably, PINNs suffer from competing losses during gradient descent that can lead to poor performance especially in physical setups involving multiple scales, which is the case for shocks in the gravitationally stratified solar atmosphere. We applied PINNs in three different setups ranging from modeling astrophysical shocks in cases with no or little data to data-intensive cases. Namely, we used PINNs (a) to determine the effective polytropic index controlling the heating mechanism of the space plasma within 1% error, (b) to quantitatively show that data assimilation is seamless in PINNs and small amounts of data can significantly increase the model’s accuracy, and (c) to solve the forward time-dependent problem for different temporal horizons. We addressed the poor performance of PINNs through an effective normalization approach by reformulating the fluid dynamics PDE system to absorb the gravity-caused variability. This led to a huge improvement in the overall model performance with the density accuracy improving between 2 and 16 times. Finally, we present a detailed critique on the strengths and drawbacks of PINNs in tackling realistic physical problems in astrophysics and conclude that PINNs can be a powerful complimentary modeling approach to classical fluid dynamics solvers.

Publisher

IOP Publishing

Subject

Artificial Intelligence,Human-Computer Interaction,Software

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. A review of unsupervised learning in astronomy;Astronomy and Computing;2024-07

2. GRINN: a physics-informed neural network for solving hydrodynamic systems in the presence of self-gravity;Machine Learning: Science and Technology;2024-04-18

3. The pulsar magnetosphere with machine learning: methodology;Monthly Notices of the Royal Astronomical Society;2024-01-17

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3