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A flow-based generative model is a generative model used in machine learning that explicitly models a probability distribution by leveraging normalizing flow, [1] [2] [3] which is a statistical method using the change-of-variable law of probabilities to transform a simple distribution into a complex one.
the normalized Ricci flow with initial data g 0 exists for all positive time and ... Understanding the possible singularity models in Ricci flow is an active research ...
Scaling of Navier–Stokes equation refers to the process of selecting the proper spatial scales – for a certain type of flow – to be used in the non-dimensionalization of the equation. Since the resulting equations need to be dimensionless, a suitable combination of parameters and constants of the equations and flow (domain ...
In another usage in statistics, normalization refers to the creation of shifted and scaled versions of statistics, where the intention is that these normalized values allow the comparison of corresponding normalized values for different datasets in a way that eliminates the effects of certain gross influences, as in an anomaly time series. Some ...
Still, turbulence models which employ the Boussinesq hypothesis have demonstrated significant practical value. In cases with well-defined shear layers, this is likely due the dominance of streamwise shear components, so that considerable relative errors in flow-normal components are still negligible in absolute terms. Beyond this, most eddy ...
The diversity of Muslims in the United States is vast, and so is the breadth of the Muslim American experience. The following animated videos depict the experiences of nine Muslim Americans from across the country who differ in heritage, age, gender and occupation.
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Over the past month and a half, Bitcoin (CRYPTO: BTC) has dominated headlines. In a matter of weeks, the world's largest cryptocurrency surged nearly 50%, ultimately breaking through the $100,000 ...