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

• URL: http://arxiv.org/abs/2607.02746v1
• Date: Thu, 02 Jul 2026 20:30:40 GMT
• Title: CodeJeNN: A simple C++ neural network generator for physics applications
• Abstract summary: Machine learning has shown speedups for numerical methods in physics applications, but integrating Python-based libraries into high-performance C++ solvers creates performance bottlenecks.<n>We present CodeJeNN, which bridges this gap by auto-generating self-contained C++ code from trained Keras models for inference.
• Score: 1.1599040452563385
• License: http://creativecommons.org/publicdomain/zero/1.0/
• 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.

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