Integration of AI-Based Nano Synergy in Bayesian Uncertainty Quantification for Advanced Engineering Design

The advancement in artificial intelligence and nanotechnology has provided new solutions for tackling problems in enhanced engineering design. This research focuses on both AI assisted observational methodologies and Bayesian uncertainty quantification (BUQ) for improving the predictive models, material properties, and design procedures. Four complex techniques of estimating and managing uncertainty are the following: Bayesian Neural Networks (BNN), Gaussian Processes (GP), Monte Carlo Dropout (MCD), Ensemble Learning (EL). Numerical studies revealed that the forecast accuracy of the proposed framework is 94.6% with BNN and 93.1% with GP, which makes excellent improvements over prior arts of up to 15% in uncertainty quantification. Besides, the computational resources are less by 20% with EL compared to standalone approaches, while the incorporation of nanoscale information increase AT and RT by 17%. To demonstrate that proposed AI-driven BUQ framework addresses the limitations of existing approaches, a comparative discussion is provided. The results reinforce its viability of providing sustainable and efficient engineering solutions under conditions of risk. This work may be used as a platform for subsequent research in the synergies between AI, nanotechnology, and the uncertainty quantification of advanced materials and complex systems to drive progress in material as well as engineering design.