Structural engineering from an inverse problems perspective
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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.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 29 Jun 2021.
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Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 2; 2025: 2; 2026: 1.
7 papers.
- 2022Principled Acceleration of Iterative Numerical Methods Using Machine Learningcited by 6
- 2024Two-Stage Surrogate Modeling for Data-Driven Design Optimization with Application to Composite Microstructure Generationcited by 2
- 2021Investigation of condominium building collapse in Surfside, Florida: A video feature tracking approachcited by 0
- 2024Machine learning for structural design models of continuous beam systems via influence zonescited by 0
- 2025Learning Latent Hardening (LLH): Enhancing Deep Learning with Domain Knowledge for Material Inverse Problemscited by 0
- 2025Toward using explainable data-driven surrogate models for treating performance-based seismic design as an inverse engineering problemcited by 0
- 2026Solving Inverse Problems of Chaotic Systems with Bidirectional Conditional Flow Matchingcited by 0
- 2026Solving Inverse Problems of Chaotic Systems with Bidirectional Conditional Flow Matching23 Jun 2026
- 2025Toward using explainable data-driven surrogate models for treating performance-based seismic design as an inverse engineering problem1 Aug 2025
- 2025Learning Latent Hardening (LLH): Enhancing Deep Learning with Domain Knowledge for Material Inverse Problems17 Jan 2025
- 2024Machine learning for structural design models of continuous beam systems via influence zones14 Mar 2024
- 2024Two-Stage Surrogate Modeling for Data-Driven Design Optimization with Application to Composite Microstructure Generation4 Jan 2024
- 2022Principled Acceleration of Iterative Numerical Methods Using Machine Learning17 Jun 2022
- 2021Investigation of condominium building collapse in Surfside, Florida: A video feature tracking approach10 Sep 2021