Accelerated ensemble optimization using momentum methods

Mathias M. Nilsen

doi:10.1038/s41598-024-76916-7

2024-10-25

Kjersti Solberg Eikrem

DOI：10.1038/s41598-024-76916-7

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摘要

En 中文

We investigate the use of various momentum methods in combination with an ensemble approximation of gradients, for accelerated optimization. Although momentum gradient descent methods are popular in machine learning, it is unclear how they perform when applied to time-consuming dynamic problems such as production optimization for petroleum reservoir management. Four different momentum methods are extensively tested on a reservoir test case in one deterministic and one robust setting. The numerical experiments show that momentum strategies yield, on average, a higher net present value with fewer simulations needed.

Keyword:

Ensemble optimization

Momentum gradient descent

Reservoir management

Dynamical optimization

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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

Scientific Reports

IF:

3.9

论文数:

27.9W

被引数:

83.5W

机构

norwegian research centre norce

学者数:

917

论文数: 771

被引数: 0

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