Hierarchical Bayesian approach to experimental data fusion: Application to strength prediction of high entropy alloys from hardness measurements
Hierarchical Bayesian approach to experimental data fusion: Application to strength prediction of high entropy alloys from hardness measurements
复制标题
实验数据融合的分层贝叶斯方法：根据硬度测量预测高熵合金的强度的应用
DOI：
10.1016/j.commatsci.2022.111851
复制
发表时间：
2023
影响因子：
3.3
通讯作者：
Bilionis, Ilias
中科院分区：
文献类型：
--
作者：
Karumuri, Sharmila;McClure, Zachary D.;Strachan, Alejandro;Titus, Michael;Bilionis, Ilias
具有组合来自多个实验信息源的数据能力的模型可以加速发现具有改进性能的材料。从业者工具箱中一个反复出现的任务是将输入物理描述符映射到感兴趣的输出属性。通常，输出和许多输入都是实验测量的，因此有噪声。概率回归方法，例如高斯过程回归，可以很容易地处理有噪声的输出，即使噪声依赖于输入。然而，大多数回归方法不能处理噪声输入。忽略输入的不确定性会导致不准确的预测不确定性，这是实验顺序设计的关键因素。本文的目的是开发一种回归方法，当人们希望将廉价的实验测量（例如硬度）与昂贵的实验测量（例如屈服强度）相关联时，该方法可以处理输入不确定性。我们的分层贝叶斯方法使用两个高斯过程。第一种方法是将无噪声的物理描述符映射到廉价的实验测量。第二个高斯过程将无噪声的物理描述符和廉价的实验测量映射到昂贵的实验测量。这两个高斯过程形成了一个嵌套的模型，无法解析处理。为了克服这个问题，我们提出了边际似然和后验预测分布的半解析近似。结果是一个训练和使用都很实用的模型。我们通过一个控制所有不确定性的合成数据集证明了所提出方法的优点。统计检验清楚地表明，标准高斯过程回归不能处理输入不确定性，而我们提出的方法始终产生更好的预测分布。最后，我们将该方法应用于根据现有文献编制的详尽数据集的硬度预测高熵合金的屈服强度的任务。
The discovery of materials with improved properties can be accelerated by models with the ability to combine data from multiple experimental information sources. A recurring task in the toolbox of practitioners is to map input physical descriptors to output properties of interest. Typically, both the outputs and many of the inputs are experimentally measured and, thus, noisy. Probabilistic regression methods, e.g., Gaussian process regression, can easily deal with noisy outputs, even if the noise is input-dependent. However, most regression methods cannot process noisy inputs. Ignoring input uncertainty leads to inaccurate predictive uncertainty, a crucial ingredient for the sequential design of experiments. The objective of this paper is to develop a regression methodology that can deal with input uncertainty when one wishes to correlate an inexpensive experimental measurement (e.g., hardness) to an expensive one (e.g., yield strength). Our hierarchical Bayesian approach uses two Gaussian processes. The first one maps noiseless physical descriptors to the inexpensive experimental measurement. The second Gaussian process maps noiseless physical descriptors and the inexpensive experimental measurement to the expensive experimental measurement. The two Gaussian processes form a nested model that is not analytically tractable. To overcome this issue, we propose semi-analytical approximations to both the marginal likelihood and the posterior predictive distribution. The result is a model that is practical to train and use. We demonstrate the merits of the proposed method through a synthetic dataset in which we control all the uncertainties. The statistical tests clearly show that standard Gaussian process regression cannot cope with input uncertainty whereas our proposed method consistently yields better predictive distributions. Finally, we apply the method to the task of predicting the yield strength of high entropy alloys from hardness on an exhaustive dataset compiled from the available literature.
登录
查看更多内容
DOI：
10.1016/j.jmrt.2020.06.036
发表时间：
2020-07-01
影响因子：
6.4
作者：
Bhandari, Uttam;Zhang, Congyan;Yang, Shizhong
通讯作者：
Yang, Shizhong
影响因子：
2.7
作者：
Bhandari, Uttam;Zhang, Congyan;Yang, Shizhong
通讯作者：
Yang, Shizhong
影响因子：
2.5
作者：
Bastos, Leonardo S.;O'Hagan, Anthony
通讯作者：
O'Hagan, Anthony
影响因子：
4.4
作者：
Denzel, Alexander;Kaestner, Johannes
通讯作者：
Kaestner, Johannes
通过高温氧化物选择的迁移学习扩大材料选择
影响因子：
2.6
作者：
McClure, Zachary D.;Strachan, Alejandro
通讯作者：
Strachan, Alejandro