Data Fidelity

Data fidelity, the accuracy and reliability of data, is crucial for effective data-driven decision-making across diverse fields. Current research focuses on improving data fidelity through methods like multi-fidelity Bayesian optimization for efficient data usage, AI-driven frameworks for automated error detection and correction, and physics-informed diffusion models for enhancing data quality in specific applications such as fluid dynamics. These advancements aim to address challenges in big data ecosystems and improve the performance of machine learning models, particularly in areas like recommendation systems and medical imaging, by enabling more robust and reliable analyses.