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About
Artificial intelligence (AI) and machine learning (ML) are having a tremendous impact across a range of subject areas including engineering and computational mechanics, which is the focus of this journal. Novel AI/ML-based methods for generating data-driven models such as Gaussian processes and convolutional neural networks offer much promise for enhancing our capability to simulate complex mechanics more accurately, to characterise fundamentally new underpinning science, and to tackle challenging mechanics problems previously deemed computationally hard. These methods are also allowing for a re-think of the way we approach computational modelling. For example, novel hybrid approaches that integrate AI/ML-based surrogates and conventional numerical models (e.g., finite element) are increasingly explored for both minimising simulation time and capturing the physics at high spatial and temporal resolutions. Another example could be the recent interest in physics-informed machine learning approaches that support creating data-driven models that satisfy the underlying physics of the system.
List of topic areas
This themed issue aims to capture such recent research developments involving the development and application of AI/ML concepts for solving applied mechanics challenges across the broad civil engineering discipline. Papers must have a significant mechanics component, i.e., have a scientific focus on subjects such as solid mechanics and fluid mechanics. Applications can include, but are not limited to, the following areas:
- Structural design and analysis
- Structural dynamics
- Digital twins
- Building performance modelling
- Fluid/structure interaction
- Soil/structure interaction.
Submissions information
If you are interested in submitting your work for the Themed Issue, please register with https://ice-review.rivervalley.io/ and then navigate to the journal that you wish to submit to. Choose article type "Themed Issue" and then the specific name from the drop-down menu on screen. Author guidelines must be strictly followed.
Full paper submissions deadline: 8th July 2024
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