CodeJeNN: A simple C++ neural network generator for physics applications
Computational Physics
2026-07-02 v1 Machine Learning
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.
Cite
@article{arxiv.2607.02746,
title = {CodeJeNN: A simple C++ neural network generator for physics applications},
author = {Jay Arcities and Pavel Popov and Eric J Ching and Kamal Viswanath and Ryan F Johnson},
journal= {arXiv preprint arXiv:2607.02746},
year = {2026}
}