Towards Reusable Surrogate Models: Graph-Based Transfer Learning on Trusses

TL;DR

This paper introduces Graph-based Surrogate Models (GSMs) for trusses that enable transfer learning across different structures, improving flexibility and data efficiency in engineering design tasks.

Contribution

The paper presents a novel graph-based surrogate modeling approach for trusses that supports transfer learning across diverse topologies and parametrizations.

Findings

GSMs accurately predict displacement fields from static loads.

Transfer learning improves model flexibility and data efficiency.

Positive knowledge transfer across different truss configurations.

Abstract

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.