  A major problem of machine-learning approaches in structural dynamics is the
frequent lack of structural data. Inspired by the recently-emerging field of
population-based structural health monitoring (PBSHM), and the use of transfer
learning in this novel field, the current work attempts to create models that
are able to transfer knowledge within populations of structures. The approach
followed here is meta-learning, which is developed with a view to creating
neural network models which are able to exploit knowledge from a population of
various tasks to perform well in newly-presented tasks, with minimal training
and a small number of data samples from the new task. Essentially, the method
attempts to perform transfer learning in an automatic manner within the
population of tasks. For the purposes of population-based structural modelling,
the different tasks refer to different structures. The method is applied here
to a population of simulated structures with a view to predicting their
responses as a function of some environmental parameters. The meta-learning
approach, which is used herein is the model-agnostic meta-learning (MAML)
approach; it is compared to a traditional data-driven modelling approach, that
of Gaussian processes, which is a quite effective alternative when few data
samples are available for a problem. It is observed that the models trained
using meta-learning approaches, are able to outperform conventional machine
learning methods regarding inference about structures of the population, for
which only a small number of samples are available. Moreover, the models prove
to learn part of the physics of the problem, making them more robust than plain
machine-learning algorithms. Another advantage of the methods is that the
structures do not need to be parametrised in order for the knowledge transfer
to be performed.
