| Summary: | <jats:title>Abstract</jats:title>
<jats:p>Assessing the fatigue damage in marine risers due to vortex-induced vibrations (VIV) serves as a comprehensive example of using machine learning methods to derive assessment models of complex systems. A complete characterization of response of such complex systems is usually unavailable despite massive experimental data and computation results. These algorithms can use multi-fidelity data sets from multiple sources, including real-time sensor data from the field, systematic experimental data, and simulation data. Here we develop a three-pronged approach to demonstrate how tools in machine learning are employed to develop data-driven models that can be used for accurate and efficient fatigue damage predictions for marine risers subject to VIV.</jats:p>
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