New Findings from Xi’an Jiaotong University in Machine Learning Provides New Ins ights (Fast Predesign Methodology of Centrifugal Compressor for Pemfcs Combining a Physics-based Loss Model and an Interpretable Machine Learning Method)

西安交通大学机器学习新发现提供了一种新的惯导系统设计方法(离心发动机的快速预设计方法基于物理损耗模型的质子交换膜燃料电池压缩机一种可解释的机器学习方法

Robotics & Machine Learning Daily News2024，Issue(Nov.8) ：72-72.

来源：

NETL
NSTL

New Findings from Xi’an Jiaotong University in Machine Learning Provides New Ins ights (Fast Predesign Methodology of Centrifugal Compressor for Pemfcs Combining a Physics-based Loss Model and an Interpretable Machine Learning Method)

西安交通大学机器学习新发现提供了一种新的惯导系统设计方法(离心发动机的快速预设计方法基于物理损耗模型的质子交换膜燃料电池压缩机一种可解释的机器学习方法

扫码查看

摘要

由一名新闻记者-机器人与机器学习日报的工作人员新闻编辑每日新闻-研究人员详细介绍机器学习的新数据。根据新闻报道来自中华人民共和国西安的NewsRx记者，研究称，“对于提振的人来说，”氢燃料电池、离心压缩机对系统效率、STAC K功率和费用。然而，离心式压缩机的设计是一个长Y和高成本的过程。

Abstract

By a News Reporter-Staff News Editor at Robotics & Machine Learning DailyNews Daily News – Researchers detail new data in Machine Learning. According to news reporting originatingfrom Xi’an, People’s Republic of China, by NewsRx correspondents, research stated, “For boostedhydrogen fuel cells, the centrifugal compressor greatly affects the system’s efficiency, stac k power andcosts. However, the design of the centrifugal compressor is a length y and high-cost process.”

Key words

Xi’an/People’s Republic of China/Asia/Cyborgs/Emerging Technologies/Machine Learning/Xi’an Jiaotong University

引用本文复制引用

出版年

2024

Robotics & Machine Learning Daily News

ISSN：

段落导航