带电粒子束的重建型AI光谱学
Reconstructive AI Spectroscopy of Charged Particle Beams
AI总结：
该研究提出基于PINN的无网格框架，结合NVIDIA PhysicsNeMo平台，可从稀疏含噪实验数据准确重建带电粒子束的复杂多峰能谱，保证物理一致性，为相关测量提供有效解决方案。
AI中文摘要：
本研究提出一种基于物理信息的神经网络（PINN）框架，用于从稀疏采样的衰减曲线数据中重建电子能谱。该方法利用NVIDIA PhysicsNeMo平台，采用无网格技术，直接处理原始实验数据集，并明确纳入所有实验不确定性。对亚纳秒电子束测量的验证表明，该方法能准确解析能量分布的复杂多峰光谱特征，通过嵌入的控制原理保证物理一致性，对含噪声、低精度及稀疏的实验数据具有强大的能谱重建预测能力。
英文摘要：
This work introduces a physics-informed neural network (PINN) framework for reconstructing electron energy spectra from sparsely sampled attenuation-curve data. Leveraging the NVIDIA PhysicsNeMo platform, the proposed mesh-free methodology operates directly on raw experimental datasets while explicitly incorporating all experimental uncertainties. Validation on subnanosecond electron beam measurements demonstrates that the approach accurately resolves complex, multi-peaked spectral features of energy distribution. The framework enforces physical consistency through embedded governing principles and exhibits substantial predictive capability for energy spectrum reconstruction from noisy, low-precision, and sparse experimental data.
