English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. Large air quality models and large climate models simulate thephysical and chemical properties of the ocean, land surface, and/oratmosphere to predict atmospheric composition, energy balance and thefuture of our planet. All of these models employ some form of operatorsplitting, also called the method of fractional steps, in their structure,which enables each physical or chemical process to be simulated in aseparate operator or module within the overall model. In this structure,each of the modules calculates property changes for a fixed period of time;that is, property values are passed into the module, which calculates howthey change for a period of time and then returns the new property values,all in round-robin between the various modules of the model. Some of thesemodules require the vast majority of the computer resources consumed by theentire model, so increasing their computational efficiency can either improvethe model's computational performance, enable more realistic physical orchemical representations in the module, or a combination of these two.Recent efforts have attempted to replace these modules with ones that usemachine learning tools to memorize the input–output relationships of themost time-consuming modules. One shortcoming of some of the original modulesand their machine-learned replacements is lack of adherence to conservationprinciples that are essential to model performance. In this work, we derivea mathematical framework for machine-learned replacements that conservesproperties – say mass, atoms, or energy – to machine precision. This frameworkcan be used to develop machine-learned operator replacements inenvironmental models.

A mass- and energy-conserving framework for using machine learning to speed computations: a photochemistry example