While deep learning has significantly advanced image reconstruction of Electrical Capacitance Tomography (ECT), most data-driven methods map directly between capacitance and permittivity distribution, treating the sensor as a black box. This overlooks the electric potential field -- the fundamental physical link governing the nonlinear and ill-posed ``soft-field'' effect. To address this, we propose an electric potential-augmented ECT benchmark dataset designed to explicitly integrate latent physics behind ECT into the learning process. Generated via a COMSOL-MATLAB pipeline for an eight-electrode sensor as an example, the dataset comprises 20,000 randomized samples across four typical flow patterns. Crucially, alongside the conventional capacitance vectors and permittivity distributions depicted as images, each sample preserves eight excitation-wise full-field potential maps. Beyond data release, we provide illustrative evaluation protocols for both forward and inverse problems of ECT. Through comprehensive testing on both in-distribution (IID) and out-of-distribution (OOD) scenarios, we systematically demonstrate how the inclusion of electric potential maps enhances modeling accuracy and robustness. Fundamentally, the explicit inclusion of latent field information significantly lowers the barrier to integrating physical laws into ECT modeling, thereby establishing a standardized foundation for future physics-guided machine learning of ECT image reconstruction.

翻译：尽管深度学习显著提升了电容层析成像（ECT）的图像重建性能，但多数数据驱动方法直接建立电容与介电常数分布之间的映射关系，将传感器视为“黑箱”。这种做法忽视了电势场——这一主导非线性、病态“软场”效应的基本物理链路。为此，我们提出一种电势增强的ECT基准数据集，旨在将ECT背后的潜在物理机制显式融入学习过程。以八电极传感器为例，通过COMSOL-MATLAB联合仿真流程生成包含四种典型流型共20,000个随机样本的数据集。关键之处在于，除常规的电容向量及以图像形式呈现的介电常数分布外，每个样本还保留了八组激励条件下的全场电势分布图。除数据发布外，我们还提供了针对ECT正反问题的示范性评估协议。通过面向同分布（IID）与异分布（OOD）场景的全面测试，系统验证了电势图的引入如何提升建模精度与鲁棒性。从根本上讲，潜在场信息的显式纳入显著降低了将物理定律融入ECT建模的门槛，从而为未来ECT图像重建的物理引导机器学习建立了标准化基础。