将训练好的Keras模型自动生成可独立运行的C++代码，提升物理模拟计算效率。
CodeJeNN: A simple C++ neural network generator for physics applications
- 通过Keras模型直接生成无外部依赖的C++推理代码
- 在流体仿真中实现加速且精度不下降
- 适合需要高性能集成的物理仿真开发人员
机器学习在物理数值方法中已展现提速潜力，但将基于Python的库集成到高性能C++求解器中会产生性能瓶颈。我们提出CodeJeNN，通过从训练好的Keras模型自动生成可独立运行的C++代码，消除外部依赖，仅通过少量内联函数实现无缝集成。本文介绍了Keras到C++的转换流程、支持的架构及局限性。通过与Keras在急切模式和JIT模式下的推理基准对比，以及氢-空气混合层粘性建模的计算流体力学（CFD）测试案例，验证了CodeJeNN在不损失精度的前提下实现速度提升。
原文摘要 · Abstract (English)
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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