Machine learning-driven strategies for managing precision and efficiency in multiphysics simulations

By employing regression algorithms, this research provides a black-box solution that predicts potential errors and computational demands, streamlining the exploration of simulation options. This method maintains the integrity of physical equations and ensures simulation convergence, reducing the trial-and-error efforts typically associated with conventional methodologies. The efficacy of this approach is demonstrated through applications in heat transfer and fluid-structure interaction problems.

Building on this, the second contribution develops an advanced framework for optimizing design parameters in scientific simulations. During our research, we identified the need for efficient parameter optimization to enhance design processes without increasing computing times. The framework employs a machine learning-based surrogate model integrated with the Non-dominated Sorting Genetic Algorithm II (NSGA-II), enabling rapid and efficient navigation of complex design spaces.

This approach balances objectives such as minimizing costs and maximizing directional motion. The effectiveness of the framework is illustrated through the optimization of a mooring system for a wave energy converter, significantly reducing the need for time-intensive simulations and facilitating informed decision-making in engineering design. Finally, recognizing the necessity of verifying simulation results to detect anomalies caused by improper parameter selection, the third contribution focuses on anomaly detection.<br><br>Unsupervised anomaly detection methods are employed to identify deviations that indicate potential convergence issues.

This approach enhances the reliability and accuracy of simulations by detecting anomalies that signal convergence failures. Validation through heat transfer simulations demonstrates the algorithm’s capability to refine parameter selection and improve the robustness of simulation processes. Together, these contributions form a cohesive framework that significantly enhances scientific simulations.

By integrating machine learning techniques for predictive accuracy, optimization, and anomaly detection, this thesis provides a comprehensive solution that improves the efficiency, reliability, and fidelity of simulation outcomes in complex engineering applications.