# Structural engineering from an inverse problems perspective

arXiv: https://arxiv.org/abs/2106.15177 · HTML: https://mlsanity.com/paper/2106.15177/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jun 2021.

## Built on by (most cited first, 7 of 7)

- [Principled Acceleration of Iterative Numerical Methods Using Machine Learning](https://mlsanity.com/paper/2206.08594/) (2022)
- [Two-Stage Surrogate Modeling for Data-Driven Design Optimization with Application to Composite Microstructure Generation](https://mlsanity.com/paper/2401.02008/) (2024)
- [Investigation of condominium building collapse in Surfside, Florida: A video feature tracking approach](https://mlsanity.com/paper/2109.06629/) (2021)
- [Machine learning for structural design models of continuous beam systems via influence zones](https://mlsanity.com/paper/2403.09454/) (2024)
- [Learning Latent Hardening (LLH): Enhancing Deep Learning with Domain Knowledge for Material Inverse Problems](https://mlsanity.com/paper/2501.10481/) (2025)
- [Toward using explainable data-driven surrogate models for treating performance-based seismic design as an inverse engineering problem](https://mlsanity.com/paper/2508.00286/) (2025)
- [Solving Inverse Problems of Chaotic Systems with Bidirectional Conditional Flow Matching](https://mlsanity.com/paper/2606.24824/) (2026)

## Abstract (arXiv)

The field of structural engineering is vast, spanning areas from the design of new infrastructure to the assessment of existing infrastructure. From the onset, traditional entry-level university courses teach students to analyse structural response given data including external forces, geometry, member sizes, restraint, etc. -- characterising a forward problem (structural causalities → structural response). Shortly thereafter, junior engineers are introduced to structural design where they aim to, for example, select an appropriate structural form for members based on design criteria, which is the inverse of what they previously learned. Similar inverse realisations also hold true in structural health monitoring and a number of structural engineering sub-fields (response → structural causalities). In this light, we aim to demonstrate that many structural engineering sub-fields may be fundamentally or partially viewed as inverse problems and thus benefit via the rich and established methodologies from the inverse problems community. To this end, we conclude that the future of inverse problems in structural engineering is inexorably linked to engineering education and machine learning developments.