Sifeng Bi,
University of Southampton, UK
Yanlin Zhao,
University of Science and Technology Beijing, China
Yongtao Bai,
Chong University, China
Yi Zhang,
Tsinghua University, China
Jie Yuan,
University of Southampton, UK
Daniil Yurchenko,
University of Southampton, UK
The realm of civil, mechanical, and aerospace engineering increasingly relies on sophisticated dynamical models to predict and analyse system behaviour under various operational and environmental conditions. These models, central to the design, monitoring and control of the performance and reliability of dynamical systems, must be rigorously calibrated, verified, and validated to ensure they meet the high standards required for safety and functionality.
Model calibration, verification, and validation (V&V) serve as foundational processes in building and maintaining trust in computational models. Calibration adjusts model parameters to ensure outputs closely match observed data, enhancing the model’s predictive capabilities. Verification checks the correctness of the model’s implementation, ensuring it accurately represents the underlying mathematical models and algorithms. Validation involves comparing the model against real-world conditions and data to confirm its accuracy and reliability. The challenge of dealing with uncertainty and nonlinearity in these systems exacerbates the complexity of the numerical modelling process, demanding robust and sophisticated V&V methodologies to manage these variables effectively.
This Mini-Symposium seeks to delve into the challenges and innovations in the numerical modelling process of dynamical systems within the engineering disciplines. With a focus on the unique difficulties presented by uncertainties and nonlinearities, the symposium will explore how advanced methods of uncertainty quantification and stochastic model updating can significantly improve the robustness and reliability of these models. Potential topics include but are not limited to the following:
Probabilistic and non-probabilistic approaches for model identification, calibration, verification, and validation tailored to uncertain and nonlinear dynamical systems.
Strategies for integrating uncertainty quantification into the modelling process to manage and mitigate risks associated with model inaccuracies.
Stochastic model updating techniques that adapt models in real-time or near-real-time to reflect new data and insights.
The application of AI and machine learning techniques to streamline and enhance the modelling processes.
The role of digital twins in synchronizing real-world data with virtual models to enhance the accuracy and timeliness of the numerical simulation process.
Detailed case studies from civil, mechanical, and aerospace engineering showcasing successful model updating and V&V practices.