.png){kind=icon}
返回
Customizable adaptive regularization techniques for B-spline modeling
.png){kind=icon}
OA
AI
DOI：10.1016/j.jocs.2023.102037.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
B-spline models are a powerful way to represent scientific data sets with a functional approximation. However, these models can suffer from spurious oscillations when the data to be approximated are not uniformly distributed. Model regularization (i.e., smoothing) has traditionally been used to minimize these oscillations; unfortunately, it is sometimes impossible to sufficiently remove unwanted artifacts without smoothing away key features of the data set. In this article, we present a method of model regularization that preserves significant features of a data set while minimizing artificial oscillations. Our method varies the strength of a smoothing parameter throughout the domain automatically, removing artifacts in poorly-constrained regions while leaving other regions unchanged. The proposed method selectively incorporates regularization terms based on first and second derivatives to maintain model accuracy while minimizing numerical artifacts. The behavior of our method is validated on a collection of two- and three-dimensional data sets produced by scientific simulations. In addition, a key tuning parameter is highlighted and the effects of this parameter are presented in detail. This paper is an extension of our previous conference paper at the 2022 International Conference on Computational Science (ICCS) (Lenz et al., 2022) [1].
Keyword:
B-spline
Regularization
Functional approximation
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
Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization耦合模型比对项目第六阶段 (CMIP6) 实验设计和组织概述OAAI
OAAI分享收藏Reconstruction of band-limited signals, irregularly sampled along one spatial direction.png)
GEOPHYSICS
GEOPHYSICSIF3.2
PREAI
分享收藏分享收藏
分享收藏
查看更多内容