DiffICF : Diffusion-Driven Inverse Modeling for Laser Pulse Design in Inertial Confinement Fusion (MLPS)

Traditional design of Laser Pulse Shapes (LPs) for Inertial Confinement Fusion (ICF) is a significant bottleneck, relying on computationally expensive simulations and manual iterative refinement. We introduce Diffusion-Driven Inverse Modeling for Laser Pulse Design (DiffICF), a generative inverse model that directly maps specified implosion outcomes to tailored LPs. DiffICF incorporates a physics-informed loss function that enforces known experimental and physical constraints. Moreover, it enables fine-grained control over pulse characteristics through constraint conditioning and inpainting. The efficacy of this framework was experimentally validated for optimizing implosion outcomes, offering a scalable, data-driven design tool to accelerate progress in fusion energy.