Meta-models for transfer learning in source localisation
This addresses the challenge of transfer learning in engineering systems where experiments are typically isolated, offering a method to leverage domain knowledge across tasks, though it appears incremental as it builds on existing multilevel modeling approaches.
The paper tackles the problem of capturing interdependencies between acoustic emission experiments for source localization in non-destructive testing, using a Bayesian multilevel meta-model to predict hyperparameters for unobserved systems, demonstrating this with an example of time-of-arrival mapping.
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).