Meta-models for transfer learning in source localisation

• URL: http://arxiv.org/abs/2305.08657v2
• Date: Fri, 08 Nov 2024 18:18:23 GMT
• Title: Meta-models for transfer learning in source localisation
• Authors: Lawrence A. Bull, Matthew R. Jones, Elizabeth J. Cross, Andrew Duncan, Mark Girolami,
• Abstract summary: This work looks to capture the interdependencies between acoustic emission (AE) experiments (as meta-models) We utilise a Bayesian multilevel approach where a higher level meta-model captures the inter-task relationships. Key contribution is how knowledge of the experimental campaign can be encoded between tasks as well as within tasks.
• Score: 3.8922067105369154
• License:
• Abstract: 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).

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