发表机构
Shanghai Jiao Tong University（上海交通大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对惯性约束聚变中离线训练代理的问题，提出Co4ICF框架，将物理信息代理与基于PPO的脉冲优化器协同进化。在1D MULTI环境有良好表现，优化脉冲在2D-MULTI中也有高归一化产额，还发布数据集，验证协同进化机制起关键作用。
AI 中文摘要
用于惯性约束聚变（ICF）的离线训练代理存在一个众所周知的故障模式，即迭代优化器会将输入驱动到预测变得不可靠的分布外（OOD）区域。在此，我们提出Co4ICF，这是一个将物理信息代理与基于近端策略优化（PPO）的脉冲优化器相结合的协同进化框架。代理在策略诱导轨迹上进行迭代微调，随着优化器改变输入分布来校正外推误差；优化器将这个不断进化的代理作为快速环境进行查询。在1D MULTI环境中,Co4ICF基于当前激光设计基线实现了146.1%的归一化产额；作为事后交叉保真度检查，在未经任何二维训练或微调的情况下直接在2D-MULTI中评估时，优化后的脉冲进一步实现了246.9%的归一化产额。预算匹配的消融实验表明，增益不仅仅由额外的模拟数据解释，并且与协同进化机制发挥关键作用一致。我们发布了一个大规模的MULTI-IFE模拟数据集以支持未来的基准测试。
英文摘要
Offline-trained surrogates for Inertial Confinement Fusion (ICF) suffer a well-known failure mode that iterative optimizers drive inputs into out-of-distribution (OOD) regions where predictions become unreliable. Here we present Co4ICF, a co-evolving framework that couples a physics-informed surrogate with a PPO-based pulse optimizer. The surrogate is iteratively fine-tuned on policy-induced trajectories, correcting extrapolation errors as the optimizer shifts the input distribution; the optimizer queries this evolving surrogate as a fast environment. In the 1D MULTI environment, Co4ICF achieves 146.1% normalized yield based on current laser design baseline; as a post-hoc cross-fidelity check, the optimized pulse further attains 246.9% normalized yield when directly evaluated in 2D-MULTI without any 2D training or fine-tuning. Budget-matched ablations support that the gains are not explained solely by additional simulation data and are consistent with the co-evolving mechanism playing a key role. We release a large-scale MULTI-IFE simulation dataset to support future benchmarking.
