通过自动诊断与技能发现训练数值智能
Training Numerical Intelligence via Auto-Diagnosis and Skill Discovery
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中文总结 AI 辅助
ADSD框架通过诊断优先范式，将数值求解器的性能问题归因并转化为可复用技能，在四个数值领域显著提升求解器精度、鲁棒性与效率，误差降低近71倍。
中文摘要 AI 辅助
人工智能代理生成科学代码的能力日益增强，但生成代码并不等同于改进其背后的算法。对于数值求解器，执行反馈可以暴露性能不佳的问题，但很少能揭示其根本原因以及如何解决。我们引入了自动诊断与技能发现（ADSD）框架，该框架将数值诊断与可复用的求解器自我改进联系起来。ADSD遵循诊断优先的范式，首先解释求解器性能不佳的原因，然后利用该诊断指导发现合适的数值方法。所得知识被打包成可复用的求解器技能，将求解器改进从试错式编辑转变为诊断、发现和实现的结构化过程。在四个具有挑战性的数值领域——潮流方程、交流最优潮流控制、刚性常微分方程和异质扩散偏微分方程——ADSD持续提高了求解器的准确性、鲁棒性和效率。例如，在GOC-500潮流问题上，ADSD将平均求解器误差降低了近71倍，且改进进一步迁移到未见过的电网拓扑和运行工况。
英文摘要
AI agents are becoming increasingly capable of generating scientific code, but generating code is not the same as improving the algorithms behind it. For numerical solvers, execution feedback can expose poor performance, but rarely reveals its underlying cause and how to address it. We introduce Auto-Diagnosis and Skill Discovery (ADSD), a framework that links numerical diagnosis to reusable solver self-improvement. ADSD follows a diagnosis-first paradigm that first explains why a solver performs poorly, then uses this diagnosis to guide the discovery of appropriate numerical methods. The resulting knowledge is packaged into reusable solver skills, turning solver improvement from trial-and-error editing into a structured process of diagnosis, discovery, and implementation. Across four challenging numerical domains--power flow equation, AC optimal power flow control, stiff ordinary differential equations, and heterogeneous diffusion PDEs--ADSD consistently improves solver accuracy, robustness, and efficiency. On GOC-500 power flow, for example, ADSD reduces mean solver error by nearly $71\times$, with improvements further transferring to unseen grid topologies and operating regimes.
发表机构
- University of California, Berkeley（加州大学伯克利分校）
- DAMO Academy, Alibaba Group U.S.（阿里巴巴集团美国达摩院）
机构由 AI 辅助整理，请以论文原文为准。
