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The field of structural engineering is vast, spanning areas from the designof new infrastructure to the assessment of existing infrastructure. From theonset, traditional entry-level university courses teach students to analysestructural response given data including external forces, geometry, membersizes, restraint, etc. -- characterising a forward problem (structuralcausalities $\to$ structural response). Shortly thereafter, junior engineersare introduced to structural design where they aim to, for example, select anappropriate structural form for members based on design criteria, which is theinverse of what they previously learned. Similar inverse realisations also holdtrue in structural health monitoring and a number of structural engineeringsub-fields (response $\to$ structural causalities). In this light, we aim todemonstrate that many structural engineering sub-fields may be fundamentally orpartially viewed as inverse problems and thus benefit via the rich andestablished methodologies from the inverse problems community. To this end, weconclude that the future of inverse problems in structural engineering isinexorably linked to engineering education and machine learning developments.

Structural engineering from an inverse problems perspective