.png){kind=icon}
返回
Solving parametric elliptic interface problems via interfaced operator network
.png){kind=icon}
OA
AI
DOI：10.1016/j.jcp.2024.113217.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Learning operators mapping between infinite -dimensional Banach spaces via neural networks has attracted a considerable amount of attention in recent years. In this paper, we propose an interfaced operator network (IONet) to solve parametric elliptic interface PDEs, where different coefficients, source terms, and boundary conditions are considered as input features. To capture the discontinuities in both the input functions and the output solutions across the interface, IONet divides the entire domain into several separate subdomains according to the interface and uses multiple branch nets and trunk nets. Each branch net extracts latent representations of input functions at a fixed number of sensors on a specific subdomain, and each trunk net is responsible for output solutions on one subdomain. Additionally, tailored physics -informed loss of IONet is proposed to ensure physical consistency, which greatly reduces the training dataset requirement and makes IONet effective without any paired input-output observations inside the computational domain. Extensive numerical studies demonstrate that IONet outperforms existing state-of-the-art deep operator networks in terms of accuracy and versatility.
Keyword:
Parametric elliptic interface problems
Interfaced operator network
Operator regression
Mesh-free method
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:
3.8
论文数:
1.6W
被引数:
7.4W
机构
引用论文
7-Eleven Japan and Toyota will launch joint convenience store project in autumn 20197-Eleven日本和丰田将在2019秋季推出联合便利店项目
PREAI
分享收藏Strategies for Reduced-Order Models for Predicting the Statistical Responses and Uncertainty Quantification in Complex Turbulent Dynamical Systems用于预测复杂湍流动力系统中统计响应和不确定性量化的降阶模型的策略.png)
SIAM REVIEWOAAI
SIAM REVIEWIF6.1
OAAI分享收藏
查看更多内容