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License: CC BY
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JAX-LaB: A Python-based, Accelerated, Differentiable Massively Parallel Lattice Boltzmann Library for Modeling Multiphase and Multiphysics Flows & Physics-Based Machine Learning

Authors: Pradhan, Piyush; Gentine, Pierre; Kelly, Shaina;

JAX-LaB: A Python-based, Accelerated, Differentiable Massively Parallel Lattice Boltzmann Library for Modeling Multiphase and Multiphysics Flows & Physics-Based Machine Learning

Abstract

This repository contains all the code, example scripts, and data required to reproduce the results shown in the manuscript: JAX-LaB: A High-Performance, Differentiable, Lattice Boltzmann Library for Modeling Multiphase Fluid Dynamics in Geosciences and Engineering, soon to be published in the Journal of Advances in Modeling Earth Systems (JAMES). Please refer to README.md and src/examples for detailed usage instructions.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average