We present a PyTorch-powered implementation of the emulator-based component analysis used for ill-posed numerical non-linear inverse problems, where an approximate emulator for the forward problem is known. This emulator may be a numerical model, an interpolating function, or a fitting function such as a neural network. With the help of the emulator and a data set, the method seeks dimensionality reduction by projection in the variable space so that maximal variance of the target (response) values of the data is covered. The obtained basis set for projection in the variable space defines a subspace of the greatest response for the outcome of the forward problem. The method allows for the reconstruction of the coordinates in this subspace for an approximate solution to the inverse problem. We present an example of using the code provided as a Python class.

翻译：我们提出了一种基于PyTorch的仿真器成分分析方法实现，该方法用于求解不适定非线性数值反问题，其中已知正向问题的近似仿真器。该仿真器可以是数值模型、插值函数或神经网络等拟合函数。借助仿真器和数据集，该方法通过在变量空间中进行投影来实现降维，从而最大程度覆盖数据目标（响应）值的方差。在变量空间中获得的投影基集定义了正向问题结果的最大响应子空间。该方法允许重建该子空间中的坐标，以近似求解反问题。我们给出了一个使用该代码作为Python类的示例。