A persistent homology framework on kernel density estimates of stationary distributions detects shifts in P-bifurcation onset and topology for a two-degree-of-freedom stochastic aeroelastic flutter model under sinusoidal, Dryden, and von Karman turbulence.
An Introduction to Topological Data Analysis: Fundamental and Practical Aspects for Data Scientists
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A neural network maps one image to a chiral spin texture whose skyrmion number equals the Euler characteristic, refined by exchange, DM, and anisotropy terms in the loss.
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P-Bifurcations in Stochastic Flutter Model Under Turbulence
A persistent homology framework on kernel density estimates of stationary distributions detects shifts in P-bifurcation onset and topology for a two-degree-of-freedom stochastic aeroelastic flutter model under sinusoidal, Dryden, and von Karman turbulence.
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Predicting Euler Characteristics and Constructing Topological Structure Using Machine Learning Techniques
A neural network maps one image to a chiral spin texture whose skyrmion number equals the Euler characteristic, refined by exchange, DM, and anisotropy terms in the loss.