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New KPPax tool enables differentiable machine learning for atmospheric chemistry

By PulseAugur Editorial · [1 sources] ·

Researchers have developed KPPax, a new preprocessor that enables the use of atmospheric chemistry mechanisms written in the Kinetic PreProcessor (KPP) language within machine learning frameworks. KPPax generates code that is differentiable, just-in-time compiled, and vectorizable using JAX. This allows for exact sensitivities to be computed through implicit solvers and adjoints from the diffrax library, facilitating parameter estimation and hybrid physics-machine learning modeling in atmospheric chemistry. AI

IMPACT Enables more sophisticated machine learning applications in atmospheric chemistry research and modeling.

RANK_REASON The cluster contains an academic paper detailing a new software tool for scientific machine learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New KPPax tool enables differentiable machine learning for atmospheric chemistry

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The cluster contains an academic paper detailing a new software tool for scientific machine learning. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Wenqing Peng, Michael Boy, Zhi-Song Liu ·

    KPPax: preprocess differentiable kinetic ODE for machine learning in atmospheric chemistry

    arXiv:2610.37655v1 Announce Type: new Abstract: Atmospheric chemical mechanisms are commonly distributed in the language of the Kinetic PreProcessor (KPP), which generates efficient Fortran or C code but does not fit into the differentiable programming frameworks in which machine…

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