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arXiv AI By Wenqing Peng, Michael Boy, Zhi-Song Liu

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

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KPPax is a new kinetic preprocessor that converts atmospheric chemistry mechanisms written in the Kinetic PreProcessor (KPP) language into differentiable, just‑in‑time compiled, vectorisable code in JAX. It evaluates rate laws in log space to maintain differentiability and integrates stiff systems using implicit solvers with forward‑ and reverse‑mode adjoints from the diffrax library, enabling exact sensitivities without hand‑derived adjoint code. Demonstrations with the POLLU benchmark show that KPPax can predict which rate coefficients are identifiable and improve neural‑network corrections of emission inventories, reducing errors by up to a factor of five.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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