用于自动神经算子发现的智能体人工智能科学社区
An Agentic AI Scientific Community for Automated Neural Operator Discovery
- Texas A&M University（德克萨斯农工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究基于人工智能科学社区提出智能体方法用于自动神经算子发现，通过虚拟实验室中三个智能体协作，共享通用词汇表，在五个问题上评估，能发现高精度低参数架构，揭示语言模型智能体对保持多样性的作用及神经算子无免费午餐定理。
AI中文摘要：
我们提出了一种基于人工智能科学社区的自主神经算子发现的智能体方法，该社区由一群虚拟实验室组成，它们在基于引用的影响经济下相互作用。高引用率的实验室会发现遵循其研究方向的新实验室，并取代表现不佳的实验室。每个虚拟实验室包含三个智能体：一个提出架构的语言模型规划器、一个训练和评估架构的数值工作者以及一个参与跨实验室同行评审的语言模型评审器。所有实验室共享一个由深度算子网络（分支 - 主干）、傅里叶、Transformer（注意力）、小波和残差卷积神经算子构建块组成的通用词汇表。我们在五个问题上评估神经算子人工智能科学社区，即分段回归、一维线性平流和伯格斯偏微分方程，以及二维纳维 - 斯托克斯和达西流偏微分方程，每个问题重复模拟三次。结果表明，神经算子人工智能科学社区能够发现高精度且低参数数量的神经算子架构。记录并审核了所有9623次语言模型调用，发现虚拟实验室语言模型规划器在99.8%的记录决策中选择进行混合，始终返回多族混合体。此外，我们通过用基于规则的替代方案替换每个实验室中的语言模型智能体进行了消融研究，这导致科学社区在几种情况下崩溃为非混合的单族堆栈，表明需要语言模型智能体来保持多样性。结果表明神经算子存在无免费午餐定理：没有通用的赢家。代码、配置和完整的语言模型记录在这个https网址发布。
英文摘要：
We present an agentic approach to autonomous neural operator discovery based on an AI scientific community, which consists of a swarm of virtual laboratories that interact under a citation-based economy of influence. Highly-cited labs found new labs that follow their research direction and replace non-performing labs. Each virtual lab contains three agents: an LLM planner that proposes an architecture, a numerical worker that trains and measures it, and an LLM reviewer that participates in cross-lab peer review. All labs share a common vocabulary consisting of DeepONet (branch-trunk), Fourier, Transformer (attention), wavelet, and residual convolutional neural operator building blocks. We evaluate the neural operator AI scientific community on five problems, namely piecewise regression, the linear advection and Burgers 1D PDEs, and the Navier-Stokes and Darcy flow 2D PDEs, while repeating the simulation three times for each problem. The results show that the neural operator AI scientific community is capable of discovering high-accuracy, low-parameter-count neural operator architectures. All 9,623 LLM calls are logged and audited, which reveals that the virtual lab LLM planners choose to hybridize in 99.8% of their logged decisions, consistently returning multi-family hybrids. Moreover, we conducted an ablation study by replacing the LLM agents in each lab by rule-based alternatives, which caused the scientific community to collapse to non-hybridized single-family stacks in several cases, showing that LLM agency is needed to preserve diversity. The results suggest a no-free-lunch theorem for neural operators: there is no universal winner. The code, configurations, and the complete LLM transcripts are released at https://github.com/luislootx/AI-SC.
