
Eccentric, nonspinning, inspiral, Gaussianprocess merger approximant for the detection and characterization of eccentric binary black hole mergers
We present ENIGMA, a time domain, inspiralmergerringdown waveform mode...
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The Physics of Eccentric Binary Black Hole Mergers. A Numerical Relativity Perspective
Gravitational wave observations of eccentric binary black hole mergers w...
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Latent Neural Differential Equations for Video Generation
Generative Adversarial Networks have recently shown promise for video ge...
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Copy the dynamics using a learning machine
Is it possible to generally construct a dynamical system to simulate a b...
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PhysicsConsistent Datadriven Waveform Inversion with Adaptive Data Augmentation
Seismic fullwaveform inversion (FWI) is a nonlinear computational imagi...
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ENIGMA: Eccentric, Nonspinning, Inspiral Gaussianprocess Merger Approximant for the characterization of eccentric binary black hole mergers
We present ENIGMA, a time domain, inspiralmergerringdown waveform mode...
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Revisit Geophysical Imaging in A New View of Physicsinformed Generative Adversarial Learning
Seismic full waveform inversion (FWI) is a powerful geophysical imaging ...
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Orbital dynamics of binary black hole systems can be learned from gravitational wave measurements
We introduce a gravitational waveform inversion strategy that discovers mechanical models of binary black hole (BBH) systems. We show that only a single time series of (possibly noisy) waveform data is necessary to construct the equations of motion for a BBH system. Starting with a class of universal differential equations parameterized by feedforward neural networks, our strategy involves the construction of a space of plausible mechanical models and a physicsinformed constrained optimization within that space to minimize the waveform error. We apply our method to various BBH systems including extreme and comparable mass ratio systems in eccentric and noneccentric orbits. We show the resulting differential equations apply to time durations longer than the training interval, and relativistic effects, such as perihelion precession, radiation reaction, and orbital plunge, are automatically accounted for. The methods outlined here provide a new, datadriven approach to studying the dynamics of binary black hole systems.
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