
This repository contains code to create forward and inverse models using Neural Physics. To help users understand the Neural Physics method, we have included some sample code in the Jupyter notebook Sample code.ipynb. The other folders contain code for the land surface model. Each of the folder contains Jupyter notebooks for the forward/inverse models, output files, and code and figures for visualising results. For code that are run in Google Colab, you may need to subscribe to Colab Pro to ensure there are sufficient computing units.
| 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). | 0 | |
| 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. | Average | |
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| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
