新方法直接从可观测数据预测结果，提升模拟效率与精度。
An Efficient Learning Method to Connect Observables
- 通过多参数特征值问题建模，实现可观测量间直接映射。
- 在1维格点系统中验证性能，可准确预测$^{28}$O的观测分布。
- 兼容EC与PMM数据，适合高维物理系统快速仿真场景。
构建快速且精确的代理模型是众多领域实现稳健预测的关键。本文提出一种新模型——多参数特征值问题（MEP）代理器，该方法能连接多个代理模型，并直接从可观测量预测可观测量。我们证明，MEP代理器可利用特征向量延续（EC）和参数化矩阵模型（PMM）生成的数据进行训练。在一维格点系统的简单模拟中，验证了MEP代理器的性能。以$^{28}$O为例，展示了通过该代理器可简便获得目标可观测量的预测概率分布。
原文摘要 · Abstract (English)
Constructing fast and accurate surrogate models is a key ingredient for making robust predictions in many topics. We introduce a new model, the Multiparameter Eigenvalue Problem (MEP) emulator. The new method connects emulators and can make predictions directly from observables to observables. We present that the MEP emulator can be trained with data from Eigenvector Continuation (EC) and Parametric Matrix Model (PMM) emulators. A simple simulation on a one-dimensional lattice confirms the performance of the MEP emulator. Using $^{28}$O as an example, we also demonstrate that the predictive probability distribution of the target observables can be easily obtained through the new emulator.
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