PhysFormer: 一种嵌入物理的生成模型，用于物理自洽的光谱合成
PhysFormer: A Physics-Embedded Generative Model for Physically Self-Consistent Spectral Synthesis
AI总结：
PhysFormer是一种嵌入物理的生成模型，通过自洽的物理过程和低维潜在空间，实现高维复杂系统的物理一致光谱合成。
摘要：
在科学和工程领域，建模由偏微分方程（PDEs）所支配的高维复杂系统在物理一致性和数值稳定性方面仍面临挑战。然而，现有方法，如物理信息神经网络（PINNs），通常依赖于已知的物理场或系数，并通过外部损失函数强制物理约束，这可能导致训练不稳定，并且难以处理高维或不可观测的场景。为此，我们提出了PhysFormer，一种生成建模框架，它在数据和物理层面都是自洽的。PhysFormer利用一个低维、物理可解释的潜在空间，直接从数据中学习关键物理量，而不需要已知的高维物理场参数，并将辐射通量生成的物理过程嵌入到网络中，以确保生成光谱的物理一致性。在高维、退化反演任务中，PhysFormer将生成限制在物理限制内，并在不同信噪比（SNRs）下增强光谱保真度和反演稳定性。更广泛地说，这种方法将物理过程从外部损失函数转移到生成机制本身，为涉及未知或不可观测物理量的复杂系统提供了一种物理一致的生成建模范式。
英文摘要：
In scientific and engineering domains, modeling high-dimensional complex systems governed by partial differential equations (PDEs) remains challenging in terms of physical consistency and numerical stability. However, existing approaches, such as physics-informed neural networks (PINNs), typically rely on known physical fields or coefficients and enforce physical constraints via external loss functions, which can lead to training instability and make it difficult to handle high-dimensional or unobservable scenarios. To this end, we propose PhysFormer, a generative modeling framework that is self-consistent at both the data and physical levels. PhysFormer leverages a low-dimensional, physically interpretable latent space to learn key physical quantities directly from data without requiring known high-dimensional physical field parameters, and embeds the physical process of radiative flux generation within the network to ensure the physical consistency of the generated spectra. In high-dimensional, degenerate inversion tasks, PhysFormer constrains generation within physical limits and enhances spectral fidelity and inversion stability under varying signal-to-noise ratios (SNRs). More broadly, this approach shifts the physical processes from external loss functions into the generative mechanism itself, providing a physically consistent generative modeling paradigm for complex systems involving unknown or unobservable physical quantities.
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