  Surrogate models have several uses in engineering design, including speeding
up design optimization, noise reduction, test measurement interpolation,
gradient estimation, portability, and protection of intellectual property.
Traditionally, surrogate models require that all training data conform to the
same parametrization (e.g. design variables), limiting design freedom and
prohibiting the reuse of historical data. In response, this paper proposes
Graph-based Surrogate Models (GSMs) for trusses. The GSM can accurately predict
displacement fields from static loads given the structure's geometry as input,
enabling training across multiple parametrizations. GSMs build upon recent
advancements in geometric deep learning which have led to the ability to learn
on undirected graphs: a natural representation for trusses. To further promote
flexible surrogate models, the paper explores transfer learning within the
context of engineering design, and demonstrates positive knowledge transfer
across data sets of different topologies, complexities, loads and applications,
resulting in more flexible and data-efficient surrogate models for trusses.
