arXiv cs.AI· Ricardo Luna Gutierrez, Vineet Gundecha, Rahman Ejaz, Varchas Gopalaswamy, Riccardo Betti, Sahand Ghorbanpour, Aarne Lees, Soumyendu Sarkar·· 12 小时前AI 评分31
ICF 激光脉冲波形设计系统 LPDS：用生成式逆建模替代昂贵仿真
Inverse Modeling for Laser Pulse Shape Design in Inertial Confinement Fusion
AI 导读
研究者提出 ICF 激光脉冲波形设计系统 LPDS，用生成式逆建模框架把目标结果与靶丸配置直接映射为优化后的激光脉冲波形，替代传统依赖昂贵仿真和人工迭代的设计流程。该系统设计了多目标损失函数以满足基础物理约束与实验要求，并提供约束条件化、inpainting 和基于梯度的波形编辑机制实现细粒度控制。研究还考察了该框架向少量真实实验打靶数据的迁移，论文已提交 NeurIPS 2026。
正文
Abstract:The achievement of practical fusion energy remains one of the most pressing unsolved scientific challenges, with immense implications for carbon-free power. A critical factor in the success of Inertial Confinement Fusion (ICF) is the design of a Laser Pulse Shape (LP) that can optimally drive implosions under stringent physical constraints. Traditional LP design relies on computationally expensive simulations and manual iterative refinement. We introduce the ICF Laser Pulse Shape Design System (LPDS), a generative inverse modeling framework that maps desired outcomes and target pellet configurations directly to optimized LPs. Crucially, we design a multi-objective loss function to ensure the generated LPs satisfy fundamental physical constraints and experimental requirements. Furthermore, we present constraint-conditioning, inpainting, and gradient-based LP editing mechanisms for fine-grained control over specific pulse characteristics during generation. Moreover, we study the transfer of our framework to a limited set of real experimental shots. Our method establishes a data-driven inverse design framework for LP design in ICF, contributing to the advancement of practical and sustainable fusion energy.
| Comments: | Initial version submitted to NeurIPS 2026 pending publication |
| Subjects: | Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Systems and Control (eess.SY); High Energy Physics - Experiment (hep-ex) |
| Cite as: | arXiv:2610.33096 [cs.AI] |
| (or arXiv:2610.33096v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2610.33096 arXiv-issued DOI via DataCite |
Submission history
From: Soumyendu Sarkar [view email]
[v1]
Thu, 22 Oct 2026 10:06:42 UTC (1,569 KB)
来源：arXiv cs.AI · arxiv.org