Large-scale, complex, and multi-functional engineering structures, such as airframes and bridges, are often exposed to severe environments for a long period of time. As such, their structural states can deviate from the initial design goal. Using force identification, damage detection, and structural health monitoring technologies, structural integrity and state change can be examined and evaluated to achieve safe operation, extended service life, and reduced maintenance costs. This type of engineering problem is a typical inverse problem, which has been significantly developed and successfully applied in aerospace, manufacturing, and infrastructure sectors.

Accuracy and efficiency are critical in inverse problems; even the simplest inverse problem may cause huge numerical error. Challenges and difficulties are faced in the following aspects: only scare measurements can be obtained from a large number of degrees of freedom; undetermined and unstable solutions are caused as the increase of the inherent ill-posedness; and uncertainty in structural parameters, measurement systems, and external environments exacerbates the difficulty of inversion. In the last few decades, many researchers have developed a variety of theories, models, and methods in this field to solve the above-mentioned challenges. Advanced sensor networks with higher accuracy and lower computational cost have been developed to update numerical models using effective surrogate models and other equivalent methods. Time or frequency domain-based inverse formulas are constituted to meet different requirements. With the development of modern intelligence algorithms and multi-functional smart materials, different types of structures suffering multi-source uncertainties in complex environments can be effectively identified and monitored gradually. Artificial Intelligence (AI) and other frontier technologies are promising to resolve the shortcomings of traditional computational inverse methods.

This Special Issue will focus on the most recent progress in advanced computational methods for inverse problems in structural engineering. The targeted readers include both academic and industrial professionals. This Special Issue aims to provide a platform to promote up-to-date research and share promising ideas in related fields. Both original research and review articles are welcomed.

Potential topics include but are not limited to the following:

• Advanced sensor networks and signal processing technologies
• Surrogate models, model order reduction, and model condensation in model updating
• Solving dynamic inverse problems in time or frequency domain
• Online/offline parameter identification in large structures
• Tikhonov, sparse, and other advanced regularization methods
• Intelligence algorithms and smart materials-based structural health monitoring
• Force and damage identification in joints, rotary parts, composite structures, etc.
• Force identification with multi-type: fixed/moving, concentrated/distributed, etc.
• Uncertainty quantification in dynamic inverse problems
• Dynamic inverse problems in the context of AI, big data, digital twin, 5G, etc.