Cosmological MBHBs coalesce in ~1 Gyr with high eccentricities; scaling relations from 30 Griffin re-simulations link dynamical-friction, hardening and total times to galaxy and orbital properties.
An Introduction to Topological Data Analysis: Fundamental and Practical Aspects for Data Scientists
5 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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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.
Vineyards from topological data analysis, checked with proposed statistical tests, visualize the evolution of local hotspots in Ohio’s overdose epidemic over time.
Investigates which quantum encodings of classical datasets preserve persistent homology so that quantum algorithms can extract topological features directly from the data.
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.
citing papers explorer
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Scaling Relations for Binary Black Hole Merger Times from Cosmological Initial Conditions
Cosmological MBHBs coalesce in ~1 Gyr with high eccentricities; scaling relations from 30 Griffin re-simulations link dynamical-friction, hardening and total times to galaxy and orbital properties.
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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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Visualizing Local Maxima of the Ohio overdose epidemic with Vineyards
Vineyards from topological data analysis, checked with proposed statistical tests, visualize the evolution of local hotspots in Ohio’s overdose epidemic over time.
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Quantum encodings that preserve persistent homology
Investigates which quantum encodings of classical datasets preserve persistent homology so that quantum algorithms can extract topological features directly from the data.
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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.