通过深度神经网络模拟电潜的不确定性方法
Erbet Almeida Costa1,2, Carine de Menezes Rebello2, Vinicius Viena Santana2
1Programa de pós-graduação em Mecatrônica, Universidade Federal da Bahia, Rua Prof. Aristides Novis, n 2., Salvador, 40210-630, Brazil.
Heliyon
|January 31, 2024
概括
本研究引入了一种用于验证人工油电梯系统深度学习模型的新方法,确保数字双胞胎和控制应用程序的可靠AI动态模型.
科学领域:
- 石油工程是石油工程中的一个.
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 使用潜水式电动的人工升油系统往往需要准确的预测模型.
- 传统的深度学习模型需要大量的数据集,对复杂的系统构成挑战.
- 评估人工智能模型的不确定性对于可靠的系统操作至关重要.
研究的目的:
- 提出一种新的方法来识别和验证人工油电梯系统的深度学习模型.
- 为了使模型性能和预测不确定性的联合和系统评估.
- 开发适合数字双胞胎构建,控制和优化的计算更轻的AI动态模型.
主要方法:
- 利用非线性模型生成合成训练和验证数据,减轻了对广泛现实世界数据集的需求.
- 采用马尔科夫链蒙特卡洛算法系统评估神经网络的认识学不确定性.
- 在Python中使用Tensorflow和Keras实现神经网络构建和超参数调整的方法.
- 根据实验数据验证开发的深度学习模型,包括不确定性评估.
主要成果:
- 提出的方法成功生成了深度学习模型,这些模型准确地代表了非线性模型的动态和实验数据.
- 模型提供了接近实验数据的最可能值,预测不确定性与非线性模型相比.
- 不确定性评估证实了开发的AI动态模型的充分验证.
- 由此产生的模型具有计算效率和可靠性.
结论:
- 开发的方法提供了一个强大的方法来创建和验证人工油电梯系统的深度学习模型.
- 人工智能动态模型适用于需要高可靠性和计算效率的应用,例如数字双胞胎.
- 这项工作推动了人工智能在优化石油和天然气生产系统中的应用.
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