A conditional variational autoencoder is trained on public kilonova light curves to enable rapid parameter inference for binary neutron star merger models in under three hours total.
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A feedforward neural network delivers probabilistic climate classifications and uncertainty estimates for the Sahara Desert from 1960-1989 data, tracking temporal shifts via fluctuation analysis.
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Precise and Rapid Parameter Inference of Kilonova with Conditional Variational Autoencoder
A conditional variational autoencoder is trained on public kilonova light curves to enable rapid parameter inference for binary neutron star merger models in under three hours total.
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Probabilistic Classification and Uncertainty Quantification of Sahara Desert Climate Using Feedforward Neural Networks
A feedforward neural network delivers probabilistic climate classifications and uncertainty estimates for the Sahara Desert from 1960-1989 data, tracking temporal shifts via fluctuation analysis.