  Sustainable energy is a crucial global challenge, and recent breakthroughs in
nuclear fusion ignition underscore the potential of harnessing energy extracted
from nuclear fusion in everyday life, thereby drawing significant attention to
fusion ignition research, especially Laser-Plasma Interaction (LPI).
Unfortunately, the complexity of LPI at ignition scale renders theory-based
analysis nearly impossible -- instead, it has to rely heavily on
Particle-in-Cell (PIC) simulations, which is extremely computationally
intensive, making it a major bottleneck in advancing fusion ignition. In
response, this work introduces Diff-PIC, a novel paradigm that leverages
conditional diffusion models as a computationally efficient alternative to PIC
simulations for generating high-fidelity scientific data. Specifically, we
design a distillation paradigm to distill the physical patterns captured by PIC
simulations into diffusion models, demonstrating both theoretical and practical
feasibility. Moreover, to ensure practical effectiveness, we provide solutions
for two critical challenges: (1) We develop a physically-informed conditional
diffusion model that can learn and generate meaningful embeddings for
mathematically continuous physical conditions. This model offers algorithmic
generalization and adaptable transferability, effectively capturing the complex
relationships between physical conditions and simulation outcomes; and (2) We
employ the rectified flow technique to make our model a one-step conditional
diffusion model, enhancing its efficiency further while maintaining high
fidelity and physical validity. Diff-PIC establishes a new paradigm for using
diffusion models to overcome the computational barriers in nuclear fusion
research, setting a benchmark for future innovations and advancements in this
field.
