Online learning of eddy-viscosity and backscattering closures for geophysical turbulence using ensemble Kalman inversion

Yifei Guan, Pedram Hassanzadeh, Tapio Schneider, Oliver Dunbar, Daniel Zhengyu Huang, Jinlong Wu, Ignacio Lopez-Gomez
{"title":"Online learning of eddy-viscosity and backscattering closures for geophysical turbulence using ensemble Kalman inversion","authors":"Yifei Guan, Pedram Hassanzadeh, Tapio Schneider, Oliver Dunbar, Daniel Zhengyu Huang, Jinlong Wu, Ignacio Lopez-Gomez","doi":"arxiv-2409.04985","DOIUrl":null,"url":null,"abstract":"Different approaches to using data-driven methods for subgrid-scale closure\nmodeling have emerged recently. Most of these approaches are data-hungry, and\nlack interpretability and out-of-distribution generalizability. Here, we use\n{online} learning to address parametric uncertainty of well-known physics-based\nlarge-eddy simulation (LES) closures: the Smagorinsky (Smag) and Leith\neddy-viscosity models (1 free parameter) and the Jansen-Held (JH)\nbackscattering model (2 free parameters). For 8 cases of 2D geophysical\nturbulence, optimal parameters are estimated, using ensemble Kalman inversion\n(EKI), such that for each case, the LES' energy spectrum matches that of direct\nnumerical simulation (DNS). Only a small training dataset is needed to\ncalculate the DNS spectra (i.e., the approach is {data-efficient}). We find the\noptimized parameter(s) of each closure to be constant across broad flow regimes\nthat differ in dominant length scales, eddy/jet structures, and dynamics,\nsuggesting that these closures are {generalizable}. In a-posteriori tests based\non the enstrophy spectra and probability density functions (PDFs) of vorticity,\nLES with optimized closures outperform the baselines (LES with standard Smag,\ndynamic Smag or Leith), particularly at the tails of the PDFs (extreme events).\nIn a-priori tests, the optimized JH significantly outperforms the baselines and\noptimized Smag and Leith in terms of interscale enstrophy and energy transfers\n(still, optimized Smag noticeably outperforms standard Smag). The results show\nthe promise of combining advances in physics-based modeling (e.g., JH) and\ndata-driven modeling (e.g., {online} learning with EKI) to develop\ndata-efficient frameworks for accurate, interpretable, and generalizable\nclosures.","PeriodicalId":501270,"journal":{"name":"arXiv - PHYS - Geophysics","volume":"2 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - PHYS - Geophysics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.04985","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Different approaches to using data-driven methods for subgrid-scale closure modeling have emerged recently. Most of these approaches are data-hungry, and lack interpretability and out-of-distribution generalizability. Here, we use {online} learning to address parametric uncertainty of well-known physics-based large-eddy simulation (LES) closures: the Smagorinsky (Smag) and Leith eddy-viscosity models (1 free parameter) and the Jansen-Held (JH) backscattering model (2 free parameters). For 8 cases of 2D geophysical turbulence, optimal parameters are estimated, using ensemble Kalman inversion (EKI), such that for each case, the LES' energy spectrum matches that of direct numerical simulation (DNS). Only a small training dataset is needed to calculate the DNS spectra (i.e., the approach is {data-efficient}). We find the optimized parameter(s) of each closure to be constant across broad flow regimes that differ in dominant length scales, eddy/jet structures, and dynamics, suggesting that these closures are {generalizable}. In a-posteriori tests based on the enstrophy spectra and probability density functions (PDFs) of vorticity, LES with optimized closures outperform the baselines (LES with standard Smag, dynamic Smag or Leith), particularly at the tails of the PDFs (extreme events). In a-priori tests, the optimized JH significantly outperforms the baselines and optimized Smag and Leith in terms of interscale enstrophy and energy transfers (still, optimized Smag noticeably outperforms standard Smag). The results show the promise of combining advances in physics-based modeling (e.g., JH) and data-driven modeling (e.g., {online} learning with EKI) to develop data-efficient frameworks for accurate, interpretable, and generalizable closures.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
利用集合卡尔曼反演在线学习地球物理湍流的涡粘度和反向散射闭合度
最近出现了不同的使用数据驱动方法进行亚网格尺度闭合建模的方法。这些方法大多对数据要求较高,缺乏可解释性和分布外概括性。在这里,我们使用在线学习来解决著名的基于物理的大尺度涡旋模拟(LES)闭合模型的参数不确定性问题:Smagorinsky(Smag)和 Leitheddy-粘度模型(1 个自由参数)以及 Jansen-Held (JH)反向散射模型(2 个自由参数)。针对 8 种二维地球物理扰动情况,利用集合卡尔曼反演(EKI)估算出最佳参数,从而使 LES 的能谱与直接数值模拟(DNS)的能谱相匹配。计算 DNS 能谱只需要少量的训练数据集(即该方法{数据效率高})。我们发现每个闭合的优化参数在不同的流态下都是恒定的,而这些流态在主要长度尺度、涡/射流结构和动力学方面都有所不同,这表明这些闭合是{可通用的}。在基于涡度的熵谱和概率密度函数(PDF)的后验中,采用优化闭合的 LES 优于基线(采用标准 Smag、动态 Smag 或 Leith 的 LES),尤其是在 PDF 的尾部(极端事件)。在先验测试中,优化 JH 在尺度间熵和能量传递方面明显优于基线、优化 Smag 和 Leith(但优化 Smag 仍明显优于标准 Smag)。这些结果表明,将基于物理的建模(如 JH)和数据驱动的建模(如使用 EKI 的{在线}学习)的进步结合起来,为准确、可解释和可推广的信息披露开发数据高效的框架是大有可为的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Groundwater dynamics beneath a marine ice sheet Generalized failure law for landslides, rockbursts, glacier breakoffs, and volcanic eruptions DiffESM: Conditional Emulation of Temperature and Precipitation in Earth System Models with 3D Diffusion Models The Arpu Kuilpu Meteorite: In-depth characterization of an H5 chondrite delivered from a Jupiter Family Comet orbit The Sun's Birth Environment: Context for Meteoritics
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1