CodeJeNN: A simple C++ neural network generator for physics applications

Abstract:Machine learning has shown speedups for numerical methods in physics applications, but integrating Python-based libraries into high-performance C++ solvers creates performance bottlenecks. We present CodeJeNN, which bridges this gap by auto-generating self-contained C++ code from trained Keras models for inference. This eliminates external dependencies through minimal inlined functions, allowing seamless integration into existing frameworks. We describe the Keras-to-C++ workflow, supported architectures, and limitations. CodeJeNN is demonstrated through inference benchmarks against Keras in eager and JIT modes and a CFD test case modeling viscosity in a hydrogen-air mixing layer, showing speedups without sacrificing accuracy.

Computational Physics (physics.comp-ph); Machine Learning (cs.LG)

arXiv:2607.02746 [physics.comp-ph]

(or arXiv:2607.02746v2 [physics.comp-ph] for this version)

https://doi.org/10.48550/arXiv.2607.02746 Focus to learn more arXiv-issued DOI via DataCite

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