mlsanityData to 7 Oct 2026

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

arXiv 2607.02746 ↗First posted 2 Jul 2026physics.comp-ph cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 16 of its 32 references that are in the map. First posted on arXiv 2 Jul 2026.

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16 of 32 references.

References · 16

  1. 2015Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classificationcited by 3,590
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  3. 2016Gaussian Error Linear Units (GELUs)cited by 3,431
  4. 2015Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)cited by 1,061
  5. 2021Physics-informed neural networks (PINNs) for fluid mechanics: A reviewcited by 470
  6. 2024Token-Budget-Aware LLM Reasoningcited by 242
  7. 2014Efficient Object Localization Using Convolutional Networkscited by 226
  8. 2016A guide to convolution arithmetic for deep learningcited by 182
  9. 2021Enhancing Computational Fluid Dynamics with Machine Learningcited by 137
  10. 2018Attention-based Graph Neural Network for Semi-supervised Learningcited by 100
  11. 2020Graph Neural Networks in TensorFlow and Keras with Spektralcited by 59
  12. 2024A review on data-driven constitutive laws for solidscited by 35
4 more
  1. 2026Tokenomics: Quantifying Where Tokens Are Used in Agentic Software Engineeringcited by 11
  2. 2024Learning incomplete factorization preconditioners for GMREScited by 9
  3. 2022A multi-scale sampling method for accurate and robust deep neural network to predict combustion chemical kineticscited by 5
  4. 2023RoseNNa: A performant, portable library for neural network inference with application to computational fluid dynamicscited by 1

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