用AI代理系统自动设计聚变燃料胶囊，实现逆向点火模拟。
Multi-Agent Design Assistant for the Simulation of Inertial Fusion Energy
- 构建多智能体系统，通过自然语言操控高保真聚变仿真代码。
- 成功在复杂物理条件下自主优化胶囊几何结构，实现模拟点火。
- 适合聚变工程与人工智能交叉研究者参考。
惯性聚变能源若能实现，将提供近乎无限且清洁的电力。但聚变系统的设计需在极端能量与时间尺度下控制物质，其行为由复杂的冲击物理与辐射输运决定，需依赖可预测的多物理场代码来应对高度非线性和多维度的设计挑战。本文提出，将人工智能推理模型与物理代码及代理模型结合，可自主设计聚变燃料胶囊。我们构建了一个多智能体系统，利用自然语言探索聚变能源的复杂物理区域。该代理系统可执行高阶多物理场惯性聚变计算代码。结果表明，该多智能体设计助手能够协作或自主地操控、导航并优化胶囊几何结构，同时考虑高保真物理机制，最终实现通过逆向设计达成模拟点火。
原文摘要 · Abstract (English)
Inertial fusion energy promises nearly unlimited, clean power if it can be achieved. However, the design and engineering of fusion systems requires controlling and manipulating matter at extreme energies and timescales; the shock physics and radiation transport governing the physical behavior under these conditions are complex requiring the development, calibration, and use of predictive multiphysics codes to navigate the highly nonlinear and multi-faceted design landscape. We hypothesize that artificial intelligence reasoning models can be combined with physics codes and emulators to autonomously design fusion fuel capsules. In this article, we construct a multi-agent system where natural language is utilized to explore the complex physics regimes around fusion energy. The agentic system is capable of executing a high-order multiphysics inertial fusion computational code. We demonstrate the capacity of the multi-agent design assistant to both collaboratively and autonomously manipulate, navigate, and optimize capsule geometry while accounting for high fidelity physics that ultimately achieve simulated ignition via inverse design.
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