In practice, non-destructive testing (NDT) procedures tend to consider experiments (and their respective models) as distinct, conducted in isolation and associated with independent data. In contrast, this work looks to capture the interdependencies between acoustic emission (AE) experiments (as meta-models) and then use the resulting functions to predict the model hyperparameters for previously unobserved systems. We utilise a Bayesian multilevel approach (similar to deep Gaussian Processes) where a higher level meta-model captures the inter-task relationships. Our key contribution is how knowledge of the experimental campaign can be encoded between tasks as well as within tasks. We present an example of AE time-of-arrival mapping for source localisation, to illustrate how multilevel models naturally lend themselves to representing aggregate systems in engineering. We constrain the meta-model based on domain knowledge, then use the inter-task functions for transfer learning, predicting hyperparameters for models of previously unobserved experiments (for a specific design).

翻译：在实践中，无损检测（NDT）程序通常将实验（及其相应模型）视为独立的、孤立进行的，并与独立数据相关联。相比之下，本研究旨在捕捉声发射（AE）实验之间的相互依赖关系（作为元模型），然后利用所得函数预测先前未观测系统的模型超参数。我们采用贝叶斯多层次方法（类似于深度高斯过程），其中高层元模型捕捉任务间关系。我们的核心贡献在于展示了如何将实验活动的知识编码在任务之间以及任务内部。我们以声发射到达时间映射用于源定位为例，说明多层次模型如何自然地适用于表示工程中的聚合系统。我们基于领域知识约束元模型，然后利用任务间函数进行迁移学习，为先前未观测实验（针对特定设计）的模型预测超参数。