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Structural identification with physics-informed neural ordinary differential equations - ScienceDirect
Source: sciencedirect.com
Article summary:
1. This paper explores a new direction of structural identification using Neural Ordinary Differential Equations (Neural ODEs) constrained by domain knowledge, such as structural dynamics.
2. The proposed framework is highly adaptive and flexible to structural monitoring problems, such as linear/nonlinear structural identification, model updating, structural damage detection, driving force identification, etc.
3. The proposed structural identification with Physics-informed Neural ODEs comes with the benefits of direct approximation of the governing dynamics and a versatile and flexible framework for discrepancy modeling in structural identification problems.
Article analysis:
This article provides an overview of a new direction of structural identification using Neural Ordinary Differential Equations (Neural ODEs). The authors present their proposed framework as being highly adaptive and flexible to various types of structural monitoring problems. They also note that the proposed approach has the benefit of direct approximation of governing dynamics and a versatile and flexible framework for discrepancy modeling in structural identification problems.
The article appears to be well-researched and provides evidence for its claims through examples from both numerical simulations and experiments on a real-world system featuring highly nonlinear behavior. Furthermore, the authors provide an additional step for inferring an explainable model by proposing the adoption of sparse identification of nonlinear dynamical systems as an additional tool to distill closed-form expressions for the trained nets.
The article does not appear to have any major biases or unsupported claims; however, it could be improved by providing more detail on how exactly the proposed framework works in practice and how it can be applied in different scenarios. Additionally, while the authors do mention potential risks associated with their approach, they do not provide any further details or discussion on this topic which could be beneficial for readers who are considering implementing this approach in their own work.