用协同演化框架提升激光聚变脉冲优化，避免模型预测失效。
Co4ICF: Co-evolving Physics-Informed Surrogate and RL-based Pulse Optimizer for Inertial Confinement Fusion
- 物理约束代理模型与强化学习优化器共同迭代更新
- 1D环境下实现146.1%的归一化产额提升，2D验证达246.9%
- 适合需要高可靠仿真优化的核聚变研究者使用
惯性约束聚变（ICF）的离线训练代理模型存在一个经典问题：迭代优化器会将输入推向分布外（OOD）区域，导致预测不可靠。本文提出Co4ICF，一种耦合物理信息代理模型与基于PPO的脉冲优化器的协同演化框架。代理模型在策略生成的轨迹上持续微调，纠正优化器迁移带来的外推误差；优化器则以该动态代理作为快速环境进行查询。在1D MULTI环境中，Co4ICF实现146.1%的归一化产额提升；作为跨保真度验证，该优化脉冲在未经过任何2D训练或微调的情况下，直接评估于2D-MULTI环境时达到246.9%的归一化产额。预算匹配的消融实验表明，性能提升不能仅由额外仿真数据解释，且协同演化机制发挥了关键作用。我们发布了大规模MULTI-IFE仿真数据集，以支持未来基准测试。
原文摘要 · Abstract (English)
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.
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