arXiv cs.AI· Ricardo Luna Gutierrez, Vineet Gundecha, Rahman Ejaz, Varchas Gopalaswamy, Riccardo Betti, Sahand Ghorbanpour, Aarne Lees, Soumyendu Sarkar·· 2 小时前AI 评分34
LPDS：面向惯性约束聚变激光脉冲形状设计的逆向建模框架
Inverse Modeling for Laser Pulse Shape Design in Inertial Confinement Fusion
AI 导读
研究者提出 ICF 激光脉冲形状设计系统 LPDS，一种生成式逆向建模框架，可将目标结果和靶丸配置直接映射为优化的激光脉冲形状。该方法通过多目标损失函数保证生成的脉冲满足物理约束和实验要求，并支持约束条件化、inpainting 和基于梯度的脉冲编辑机制实现细粒度控制，还在有限的真实实验打靶数据上验证了框架迁移能力，已提交 NeurIPS 2026。
正文 · 原文
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