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on correlation-based machine learning. When an agricultural system fails due to compounding climate extremes - like a simultaneous heatwave, drought, and ozone pollution spike - standard models can forecast the
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- and risk optimal installation and operation of offshore wind farms depend on accurate forecasts and predictions of environmental conditions and response of vessel and auxiliary systems. The focus
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many approaches in explainable AI and neuro-symbolic methods focus on similar issues, they are prone to learning concepts with the wrong intended meaning, especially when different concepts
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around us evolve over both time and space, making spatio-temporal processes and data omnipresent in science and technology, with applications ranging from weather forecasting to cardiovascular medicine
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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