arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2512.03476cs.LGcs.AIcs.MAcs.NAmath.NAphysics.comp-ph
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ATHENA:分层进化数值算法的智能团队

ATHENA: Agentic Team for Hierarchical Evolutionary Numerical Algorithms

Division of Applied Mathematics, Brown University(布朗大学应用数学系)

Juan Diego Toscano, Daniel T. Chen, George Em Karniadakis

AI总结:

ATHENA通过知识驱动的诊断流程,实现科学计算与科学机器学习的自动化,结合专家蓝图和组合空间,生成高精度数值解和稳定求解器,达到10^-14的验证误差。

摘要:

在科学计算和科学机器学习中,理论概念与计算实现之间的差距是主要瓶颈。我们介绍了ATHENA(分层进化数值算法的智能团队),一个作为自主实验室的智能框架,用于管理端到端的计算研究生命周期。其核心是HENA循环,一种基于上下文老虎机问题的知识驱动诊断过程。系统作为在线学习者,分析先前试验,从组合空间中选择结构‘动作’(A_n),并将其转换为可执行代码(S_n)以生成科学奖励(R_n)。ATHENA超越了标准自动化:在科学计算中,它能够自动识别数学对称性以获得精确解析解或推导稳定数值求解器,即使基础模型失效。在科学机器学习中,它执行深度诊断以处理不恰当的公式,并结合混合的符号-数值工作流程(例如将PINNs与FEM耦合)以解决多物理场问题。该框架实现了超人类性能,达到10^-14的验证误差。此外,协作的‘人机协同’干预使系统能够弥合稳定性缺口,将结果提高一个数量级。这一范式转变从实现机制转向方法论创新,加速了科学发现。

英文摘要:

Progress in computational science depends on complex numerical workflows that must faithfully encode physical laws, yet translating conceptual insight into reliable code remains a major bottleneck. Although large language models can generate isolated code fragments, they lack the structured reasoning required to design, verify, and iteratively refine complete scientific pipelines. Here we introduce ATHENA, an agentic framework explicitly designed to emulate scientific research modeled as a knowledge-driven contextual bandit process. Its core loop separates conceptual policy from numerical realization through expert-derived conceptual scaffolding, enabling principled diagnosis, reformulation, and repair of computational strategies. Across scientific computing and scientific machine learning tasks, ATHENA autonomously derives and correctly applies exact analytical solutions, constructs stable numerical solvers, diagnoses ill-posed formulations, and orchestrates hybrid symbolic-numeric workflows. Quantitatively, ATHENA matches and frequently surpasses the accuracy of expert-authored reference solutions reported in the literature on canonical benchmarks. By reframing computation as an object of agentic reasoning, our framework enables autonomous orchestration of heterogeneous algorithms across scientific domains.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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