寻找PDE问题的机器学习解决方案

IF 18.8 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Nature Machine Intelligence Pub Date : 2025-01-27 DOI:10.1038/s42256-025-00989-w

引用次数: 0

摘要

机器学习模型是解决偏微分方程的有前途的方法，偏微分方程是许多科学和工程问题的基础描述。然而，在与几位专家谈论该领域的进展时，关于机器学习模型具有哪些现实优势以及如何评估其性能的问题正在出现。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Machine learning solutions looking for PDE problems

Machine learning models are promising approaches to tackle partial differential equations, which are foundational descriptions of many scientific and engineering problems. However, in speaking with several experts about progress in the area, questions are emerging over what realistic advantages machine learning models have and how their performance should be evaluated.

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来源期刊

Nature Machine Intelligence Multiple-

CiteScore

36.90

自引率

2.10%

发文量

127

期刊介绍： Nature Machine Intelligence is a distinguished publication that presents original research and reviews on various topics in machine learning, robotics, and AI. Our focus extends beyond these fields, exploring their profound impact on other scientific disciplines, as well as societal and industrial aspects. We recognize limitless possibilities wherein machine intelligence can augment human capabilities and knowledge in domains like scientific exploration, healthcare, medical diagnostics, and the creation of safe and sustainable cities, transportation, and agriculture. Simultaneously, we acknowledge the emergence of ethical, social, and legal concerns due to the rapid pace of advancements. To foster interdisciplinary discussions on these far-reaching implications, Nature Machine Intelligence serves as a platform for dialogue facilitated through Comments, News Features, News & Views articles, and Correspondence. Our goal is to encourage a comprehensive examination of these subjects. Similar to all Nature-branded journals, Nature Machine Intelligence operates under the guidance of a team of skilled editors. We adhere to a fair and rigorous peer-review process, ensuring high standards of copy-editing and production, swift publication, and editorial independence.