发表机构
Imperial College London（帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对少量传感器下多尺度等离子体动力学重构难题，提出ReMAIN模型，结合循环编码与条件化U-Net，在6种PDE及E×B等离子体上实现跨工况的高精度全状态重构与泛化。
AI 中文摘要
从少量传感器重构空间分辨的等离子体动力学对诊断、降阶建模和控制至关重要，但由于稀疏测量无法完全约束多尺度、依赖工况的自由度，该任务仍具挑战性。浅层循环解码器（SHRED）通过利用测量历史部分解决了空间稀疏性问题，但其全连接解码器缺乏明确的跨尺度空间结构解析机制或显式参数依赖。我们提出循环多尺度仿射调制推理网络（ReMAIN），该网络保留了SHRED的循环时间编码，但其解码器替换为U-Net，U-Net的特征层级通过逐特征线性调制由循环状态调节。时间表示为整个U-Net提供了密集先验和特定尺度的调制。参数扩展模块联合嵌入运行工况和传感器历史，使重构能随控制动力学随运行工况的变化而自适应调整。首先在6种代表不同动力学的一维非线性偏微分方程上，将ReMAIN与SHRED进行基准测试，在所有基准测试中，ReMAIN降低了未见轨迹的重构误差，且更忠实地解析了尖锐过渡、局部极值和细尺度变化。随后在受垂直轴向电场和径向磁场作用的无碰撞E×B等离子体上验证了该参数条件模型，其中电场强度作为运行参数。ReMAIN重构了高维多尺度等离子体状态，并在训练时未使用的电场强度下恢复了其依赖工况的时空动力学。总体而言，ReMAIN改进了稀疏传感器的全状态重构，并通过参数条件化实现了跨等离子体运行工况的泛化。
英文摘要
Reconstructing spatially resolved plasma dynamics from few sensors is essential for diagnostics, reduced-order modelling and control, yet remains difficult because the sparse measurements incompletely constrain multiscale, regime-dependent degrees of freedom. The Shallow Recurrent Decoder (SHRED) partially addresses spatial sparsity by using measurement histories; however, its fully connected decoder provides no explicit mechanism for resolving spatial structure across scales or explicit parametric dependency. We introduce the Recurrent Multiscale Affine-modulated Inference Network (ReMAIN), which preserves SHRED's recurrent temporal encoding but replaces its decoder with a U-Net whose feature hierarchy is conditioned by the recurrent state through feature-wise linear modulation. The temporal representation supplies both a dense prior and scale-specific modulation throughout the U-Net. A parametric extension jointly embeds the operating condition and sensor history, enabling reconstruction to adapt as the governing dynamics change with operating regime. ReMAIN is first benchmarked against SHRED on six one-dimensional nonlinear PDEs representing diverse dynamics. Across all benchmarks, it reduces reconstruction errors on unseen trajectories and more faithfully resolves sharp transitions, localized extrema and fine-scale variations. The parameter-conditioned model is then demonstrated on a collisionless $E \times B$ plasma subject to perpendicular axial electric and radial magnetic fields, with the electric-field strength serving as the operating parameter. ReMAIN reconstructs the high-dimensional, multiscale plasma state and recovers its regime-dependent spatiotemporal dynamics at electric-field strengths withheld from training. Together, ReMAIN improves sparse-sensor full-state reconstruction and, through parameter conditioning, generalizes across plasma operating regimes.
Comments27 pages, 17 figures
