2023

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JFM Rapids: The Editors’ Insights

A spotlight on JFM Rapids, a well-established section in the Journal of Fluid Mechanics [JFM] that continues to provide a highly visible venue for short, high-quality, articles addressing timely research challenges of broad interest. In this collection, the Editors of JFM Rapids each explain why they selected one article that presents exciting results with exceptional impact on currently active fluid mechanics research.

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When the Levee Forms

Blues fans out there may be familiar with the Led Zeppelin classic ‘When the Levee Breaks’, but what about when the levee forms? In particular, how do natural levees form in such an organised and well-engineered process?

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The latest research in training modern machine learning models: ‘A deterministic modification of gradient descent that avoids saddle points

Machine learning models, particularly those based on deep neural networks, have revolutionized the fields of data analysis, image recognition, and natural language processing. A key factor in the training of these models is the use of variants of gradient descent algorithms, which optimize model parameters by minimizing a loss function. However, the training optimization problem for neural networks is highly non-convex, presenting unique challenges.

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