The rapid development of AI highlights the pressing need for sustainable energy, a critical global challenge for decades. Nuclear fusion, generally seen as a promising solution, has been the focus of intensive research for nearly a century, with investments reaching hundreds of billions of dollars. Recent advancements in Inertial Confinement Fusion (ICF) have drawn significant attention to fusion research, in which Laser-Plasma Interaction (LPI) is critical for ensuring fusion stability and efficiency. However, the complexity of LPI makes analytical approaches impractical, leaving researchers dependent on extremely computationally intensive Particle-in-Cell (PIC) simulations to generate data, posing a significant bottleneck to the advancement of fusion research. In response, this work introduces Diff-PIC, a novel framework that leverages conditional diffusion models as a computationally efficient alternative to PIC simulations for generating high-fidelity scientific LPI data. In this work, physical patterns captured by PIC simulations are distilled into diffusion models associated with two tailored enhancements: (1) To effectively capture the complex relationships between physical parameters and their corresponding outcomes, the parameters are encoded in a physically informed manner. (2) To further enhance efficiency while maintaining physical validity, the rectified flow technique is employed to transform our model into a one-step conditional diffusion model. Experimental results show that Diff-PIC achieves a $\sim$16,200$\times$ speedup compared to traditional PIC on a 100 picosecond simulation, while delivering superior accuracy compared to other data generation approaches.

Controlled fusion energy is deemed pivotal for the advancement of human civilization. In this study, we introduce LPI-LLM, a novel integration of Large Language Models (LLMs) with classical reservoir computing paradigms tailored to address a critical challenge, Laser-Plasma Instabilities (), in Inertial Confinement Fusion (). Our approach offers several key contributions: Firstly, we propose the LLM-anchored Reservoir, augmented with a Fusion-specific Prompt, enabling accurate forecasting of -generated-hot electron dynamics during implosion. Secondly, we develop Signal-Digesting Channels to temporally and spatially describe the driver laser intensity across time, capturing the unique characteristics of inputs. Lastly, we design the Confidence Scanner to quantify the confidence level in forecasting, providing valuable insights for domain experts to design the process. Extensive experiments demonstrate the superior performance of our method, achieving 1.90 CAE, 0.14 MAE, and 0.11 MAE in predicting Hard X-ray () energies emitted by the hot electrons in implosions, which presents state-of-the-art comparisons against concurrent best systems. Additionally, we present LPI4AI, the first benchmark based on physical experiments, aimed at fostering novel ideas in research and enhancing the utility of LLMs in scientific exploration. Overall, our work strives to forge an innovative synergy between AI and for advancing fusion energy.