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用贝叶斯神经网络进行材料性质预测的多变量回归中的不确定性量化
Longze Li1, Jiang Chang1, Aleksandar Vakanski2
1Department of Computer Science, University of Idaho, Idaho Falls, ID, 83404, USA.
Scientific reports
|May 8, 2024
概括
基于物理学的贝叶斯神经网络 (BNNs) 为材料属性预测提供可靠的不确定性量化. 这种在钢材爬行寿命方面得到验证的方法,优于传统方法,增强了积极学习策略.
科学领域:
- 材料科学 材料科学 材料科学
- 计算材料科学科学 计算材料科学
- 机器学习 机器学习
背景情况:
- 数据驱动的方法和机器学习在材料科学中越来越重要.
- 可靠的不确定性量化 (UQ) 对于在物质属性预测中做出知情决策至关重要.
- 对材料的UQ的挑战包括多尺度物理,复杂的相互作用和有限的数据.
研究的目的:
- 为材料科学中UQ引入一种新的基于物理的贝叶斯神经网络 (BNNs) 方法.
- 评估这种BNNs方法对预测钢合金的爬行破裂寿命的有效性.
- 评估UQ在主动学习场景中的表现,用于材料预测.
主要方法:
- 开发了一种基于物理的贝叶斯神经网络 (BNNs) 框架,集成物质治理规律.
- 应用了BNNs方法来预测使用三个实验性钢合金数据集的爬行破裂寿命.
- 与高斯过程回归和其他神经网络变体比较BNN的性能.
主要成果:
- 与传统的UQ方法相比,基于物理的BNNs方法显示出具有竞争力或优异的点预测和不确定性估计.
- 在材料预测的积极学习场景中,BNNs框架显示出有希望的表现.
- 使用马尔科夫链蒙特卡洛 (MCMC) 近似的贝叶斯神经网络比变化推理近似产生了更可靠的结果.
结论:
- 基于物理学的BNN为材料性质预测中的不确定性量化提供了强大的框架.
- 基于MCMC的BNNs方法对于准确可靠的爬行生命预测特别有效.
- 这种方法通过提供可靠的预测和不确定性估计来增强机器学习在材料科学中的实用性.
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