This study presents a comprehensive investigation of U-Net-based graph neural networks (Graph U-Nets) for mesh-agnostic spatio-temporal forecasting of unsteady flow fields. We systematically adapt and enhance Graph U-Nets, originally developed for classification tasks, for high-dimensional regression problems in fluid dynamics through extensive architectural modifications and hyperparameter optimization. Key enhancements include the implementation of Gaussian mixture model convolutional operators, which provide superior flexibility in modeling node dynamics and reduce prediction error by 95% compared to conventional graph convolutional operators. Additionally, we introduce noise injection strategies that significantly improve long-term prediction robustness, achieving an 86% reduction in temporal prediction error. Through comprehensive ablation studies, we investigate the effects of pooling strategies, normalization techniques, and architectural choices on model performance. We demonstrate the framework’s effectiveness in both transductive learning settings—successfully predicting flow fields in unseen spatial regions—and inductive learning scenarios across diverse mesh configurations with varying vortex shedding dynamics. Notably, we discover that optimal inductive performance requires eliminating pooling operations and employing layer normalization, contrary to single-mesh scenarios. The enhanced Graph U-Net successfully generalizes to unseen mesh scenarios, achieving improved prediction accuracy for challenging slow-vortex-shedding cases when trained on diverse flow regimes. This work establishes Graph U-Nets as a viable and flexible alternative to convolutional neural networks for computational fluid dynamics applications, demonstrating their potential for real-time flow prediction in digital twin frameworks across diverse industrial scenarios.

As autonomous-vehicle technologies advance, conventional taxi and car-sharing services are being combined into a shared autonomous vehicle service, and through this, it is expected that the transition to a new paradigm of shared mobility will begin. However, before the full development of technology, it is necessary to accurately identify the needs of the service’s users and prepare customer-oriented design guidelines accordingly. This study is concerned with the following problems: (1) How should an autonomous taxi service be designed and field-tested if the self-driving technology is imperfect? (2) How can imperfect self-driving technology be supplemented by using service flexibility? This study implements an autonomous taxi service prototype through a Wizard of Oz method. Moreover, by conducting field tests with scenarios involving an actual taxi, this study examines customer pain points, and provides a user-experience-based design solutions for resolving them.