Modern scientific and engineering challenges increasingly demand approaches that blend data-centric methods with traditional analytical models. As systems grow in complexity, harnessing vast datasets and extracting actionable insights becomes crucial for effective modeling, prediction, and control. By integrating machine learning with dynamical systems theory, practitioners can develop robust frameworks that not only describe but also anticipate and influence real-world phenomena across disciplines.

• Data-driven modeling augments or supplants traditional first-principles modeling, especially when physical laws are partially unknown or systems are too complex for closed-form solutions.
• Machine learning techniques, such as regression, classification,...
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