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Field
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conceptualized as a specialized form of anomaly detection. Specifically, the objective is to identify anomalies that evolve gradually and to forecast the time-to-failure with sufficient accuracy. Consequently
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well as machine learning, is ensuring reliability and trustworthiness. This is especially crucial in applications such as medical diagnosis, weather forecasting, and aircraft design. To improve the reliability and
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estimation of RUL can be conceptualized as a specialized form of anomaly detection. Specifically, the objective is to identify anomalies that evolve gradually and to forecast the time-to-failure with
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the emerging IHO S100 maritime data standard together with historical, real-time and forecast information including shipping traffic, navigational hazards, bathymetry, and metocean conditions such as waves
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weather forecasting to cardiovascular medicine. Computational tools for simulating such processes - both traditional based e.g. on computational fluid dynamics and more recent based on AI/machine learning
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machine learning frameworks such as recurrent neural networks and transformers. Models and datasets will be studied and benchmarked in key tasks relating to both prediction/forecasting and anomaly detection