Machine Learning With Data Assimilation and Uncertainty Quantification for Dynamical Systems: A Review

Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical applications span from computational fluid dynamics (CFD) to geoscience and climate systems. Recently, much effort has been given in combining DA, UQ and machine learning (ML) techniques. These research efforts seek to address some critical challenges in high-dimensional dynamical systems, including but not limited to dynamical system identification, reduced order surro-gate modelling, error covariance specification and model error correction. A large number of developed techniques and methodologies exhibit a broad applicability…

• LT
  Leverhulme Trust
• DF
  Deutsche Forschungsgemeinschaft

  Award: JA 1077/4-1
• NN
  National Natural Science Foundation of China

  Award: 61976120
• EA
  Engineering and Physical Sciences Research Council

  Awards: EP/V000756/1, EP/V040235/1, EP/V000756/1
• FR
  Fundamental Research Funds for the Central Universities