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<p>The creation of synthetic data has become an increasingly important method for addressing data constraints in artificial intelligence, but its dependability primarily relies on proficient uncertainty management. This systematic review introduces a new categorization for uncertainty-aware synthetic data generation, dividing methods into three main paradigms: probabilistic approaches using Bayesian frameworks for thorough uncertainty propagation; generative architectures with neural uncertainty quantification; and hybrid systems combining physical constraints with data-driven techniques. The survey evaluates how these frameworks tackle both intrinsic data variability and model-related uncertainties, including applications in data imputation, while pinpointing ongoing issues in computational efficiency, privacy protection, and distribution alignment. Our analysis of peer-reviewed studies yields three primary contributions: (1) a novel hierarchical taxonomy categorizing methods into probabilistic, generative, and hybrid paradigms with clear distinctions between their uncertainty quantification mechanisms; (2) a comparative analysis revealing that method effectiveness varies by data modality—with diffusion models excelling for images (FID 5–20), Bayesian methods dominating tabular applications (58% of studies), and physics-informed approaches achieving 20–40% better extrapolation for time-series data; and (3) identification of critical validation gaps, as only 34% of studies empirically validate uncertainty estimates. The review uncovers notable deficiencies in standardization among methodologies and suggests measures to improve synthetic data reliability, including adaptive uncertainty calibration and domain-aware validation protocols. For researchers, this study offers a systematic framework highlighting the importance of theoretically-based uncertainty quantification, strong evaluation benchmarks, and transparent generation processes. Professionals will benefit from practical guidance for selecting suitable methods based on application-specific needs.</p>

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Uncertainty-aware synthetic data generation: a systematic review of methods and challenges

  • 期刊论文

  • 发表日期：

    2026年4月17日

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  • Amir Hosein Keyhanipour

摘要

The creation of synthetic data has become an increasingly important method for addressing data constraints in artificial intelligence, but its dependability primarily relies on proficient uncertainty management. This systematic review introduces a new categorization for uncertainty-aware synthetic data generation, dividing methods into three main paradigms: probabilistic approaches using Bayesian frameworks for thorough uncertainty propagation; generative architectures with neural uncertainty quantification; and hybrid systems combining physical constraints with data-driven techniques. The survey evaluates how these frameworks tackle both intrinsic data variability and model-related uncertainties, including applications in data imputation, while pinpointing ongoing issues in computational efficiency, privacy protection, and distribution alignment. Our analysis of peer-reviewed studies yields three primary contributions: (1) a novel hierarchical taxonomy categorizing methods into probabilistic, generative, and hybrid paradigms with clear distinctions between their uncertainty quantification mechanisms; (2) a comparative analysis revealing that method effectiveness varies by data modality—with diffusion models excelling for images (FID 5–20), Bayesian methods dominating tabular applications (58% of studies), and physics-informed approaches achieving 20–40% better extrapolation for time-series data; and (3) identification of critical validation gaps, as only 34% of studies empirically validate uncertainty estimates. The review uncovers notable deficiencies in standardization among methodologies and suggests measures to improve synthetic data reliability, including adaptive uncertainty calibration and domain-aware validation protocols. For researchers, this study offers a systematic framework highlighting the importance of theoretically-based uncertainty quantification, strong evaluation benchmarks, and transparent generation processes. Professionals will benefit from practical guidance for selecting suitable methods based on application-specific needs.