.png){kind=icon}
返回
Memory-efficient emulation of physical tabular data using quadtree decomposition
.png){kind=icon}
OA
AI
DOI：10.1016/j.jocs.2022.101823.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Computationally expensive functions are sometimes replaced in simulations with an emulator that approx-imates the true function (e.g., equations of state, wavelength-dependent opacity, or composition-dependent materials properties). For functions that have a constrained domain of interest, this can be done by discretizing the domain and performing a local interpolation on the tabulated function values of each local domain. For these so-called tabular data methods, the method of discretizing the domain and mapping the input space to each subdomain can drastically influence the memory and computational costs of the emulator. This is especially true for functions that vary drastically in different regions. We present a method for domain discretization and mapping that utilizes quadtrees, which results in significant reductions in the size of the emulator with minimal increases to computational costs or loss of global accuracy. We apply our method to the electron-positron Helmholtz free energy equation of state and show over an order of magnitude reduction in memory costs for reasonable levels of numerical accuracy.
Keyword:
Lookup table
Tabular data
Equation of state
Emulator
Quadtree
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
18.3
论文数:
3.1K
被引数:
4.0K
机构
引用论文
.png){kind=icon}
Intraoperative Evaluation of Breast Tumor Margins with Optical Coherence Tomography术中光学相干层析成像对乳腺肿瘤切缘的评估
PREAI
分享收藏
查看更多内容