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Integrating physical units into high-performance AI-driven scientific computing

Author

Listed:
  • Chaoming Wang

    (Peking University)

  • Sichao He

    (Peking University)

  • Shouwei Luo

    (Shanghai Jiao Tong University
    Shanghai Jiao Tong University)

  • Yuxiang Huan

    (Guangdong Institute of Intelligence Science and Technology)

  • Si Wu

    (Peking University
    Peking University
    Guangdong Institute of Intelligence Science and Technology
    Peking University)

Abstract

Artificial intelligence is revolutionizing scientific research across various disciplines. The foundation of scientific research lies in rigorous scientific computing based on standardized physical units. However, current mainstream high-performance numerical computing libraries for artificial intelligence generally lack native support for physical units, significantly impeding the integration of artificial intelligence methodologies into scientific research. To fill this gap, we introduce SAIUnit, a system designed to seamlessly integrate physical units into scientific artificial intelligence libraries, with a focus on compatibility with JAX. SAIUnit offers a comprehensive library of over 2000 physical units and 500 unit-aware mathematical functions. It is fully compatible with JAX transformations, allowing for automatic differentiation, just-in-time compilation, vectorization, and parallelization while maintaining unit consistency. We demonstrate SAIUnit’s applicability and effectiveness across diverse artificial intelligence-driven scientific computing domains, including numerical integration, brain modeling, and physics-informed neural networks. Our results show that by confining unit checking to the compilation phase, SAIUnit enhances the accuracy, reliability, interpretability, and collaborative potential of scientific computations without compromising runtime performance. By bridging the gap between abstract computing frameworks and physical units, SAIUnit represents a crucial step towards more robust and physically grounded artificial intelligence-driven scientific computing.

Suggested Citation

  • Chaoming Wang & Sichao He & Shouwei Luo & Yuxiang Huan & Si Wu, 2025. "Integrating physical units into high-performance AI-driven scientific computing," Nature Communications, Nature, vol. 16(1), pages 1-14, December.
    • Handle: RePEc:nat:natcom:v:16:y:2025:i:1:d:10.1038_s41467-025-58626-4
    DOI: 10.1038/s41467-025-58626-4
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    References listed on IDEAS

    as
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