CodeJeNN: A simple C++ neural network generator for physics applications
Jay Arcities, Pavel Popov, Eric J Ching, Kamal Viswanath, Ryan F Johnson
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.
Topics
Classified with taxonomy v2 on Sat, 5 Sept 2026.