A survey on machine learning approaches for uncertainty quantification of engineering systems

Abstract Uncertainty quantification (UQ) is essential for understanding and mitigating the impact of pervasive uncertainties in engineering systems, playing a crucial role in modern engineering practice. As engineering products grow increasingly complex and the demand for highly accurate UQ results intensifies, the need for efficient UQ methods has become paramount. Recently, machine learning (ML) techniques, including Gaussian process regression, artificial neural networks, physics-informed neural networks, and many others, have garnered significant attention in both theoretical research and practical applications. The exceptional capability of ML methods to address highly complex problems has positioned them…

• AV
  Alexander von Humboldt-Stiftung
• NN
  National Natural Science Foundation of China

  Awards: 52475164, 52205252, M-0175
• GW
  Gottfried Wilhelm Leibniz Universität Hannover