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This workflow is applied in a case study.
The scientific relevance of this workflow is also discussed.
This workflow is based on backpropagation starting from a material balance for a certain target variable.
A consequence of this workflow is that an organization's tasks become entangled in a complicated network of dependencies with inbox-enslaved individuals sited at each node.
This workflow is applied to the cyclic pressure pulsing process with CO2 and N2 in naturally fractured reservoirs.
This workflow is implemented to investigate a room served by different Parallel Slat Transparent Insulation Materials (PS-TIM), which represents an example of a complex fenestration system.
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As a proof-of-concept, this workflow was applied to discriminate between Escherichia coli mutants with gradual expression of the glucose-6-phosphate dehydrogenase.
The granulized components of this workflow are in two major areas: Knowledge Area (KA) and Execution Area (ExeA).
The development of this workflow was motivated by three main factors: (1) the screening of compound collections is a widely used starting point in many drug discovery projects.
So the challenge for Adobe and Nvidia (which provided the hardware for this workflow) was to enable all of this data to flow with as little latency as possible.
The main three steps of this workflow are (a) data preparation and modification; (b) data process and workflow; and (c) user interface and data access as detailed in Sects.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com