In AI and computational models, scale separation involves differentiating between data or processes that occur at significantly different scales, which can be crucial for simplifying models, enhancing computational efficiency, and improving the interpretability of results. For instance, in machine learning, features or signals might operate at varying scales that influence how models are trained and how data normalization is applied. Scale separation is also integral to multiscale modeling, where processes at a smaller scale (like molecular interactions) can be abstractly represented to impact outcomes at a larger scale (such as tissue behavior in biomedical applications).
Historical Overview: The principle of scale separation has been implicitly used in various scientific fields for decades but has seen explicit formulation and widespread application particularly since the 1970s, as computational models began to handle complex, multiscale phenomena more frequently.
Key Contributors: While specific contributors to the concept of scale separation are hard to pinpoint due to its broad usage across disciplines, it has been significantly shaped by researchers in fields like meteorology, physics, and later in computational biology and AI, where researchers have developed techniques to handle data and model systems at varying scales effectively.