论文标题
物理受限的间接监督学习
Physics-constrained indirect supervised learning
论文作者
论文摘要
这项研究提出了一种不依赖标签的监督学习方法。我们将与标签相关的变量用作间接标签，并基于训练模型的物理机制构建间接物理受限的损失。在训练过程中，模型预测映射到通过投影矩阵符合物理机制的价值空间，然后根据间接标签对模型进行训练。该模型的最终预测结果符合间接标签和标签之间的物理机制，并且还符合间接标签的约束。本研究还开发了投影矩阵的归一化和预测协方差分析，以确保可以对模型进行全面训练。最后，根据井的生成问题，验证了物理受限的间接学习的效果。
This study proposes a supervised learning method that does not rely on labels. We use variables associated with the label as indirect labels, and construct an indirect physics-constrained loss based on the physical mechanism to train the model. In the training process, the model prediction is mapped to the space of value that conforms to the physical mechanism through the projection matrix, and then the model is trained based on the indirect labels. The final prediction result of the model conforms to the physical mechanism between indirect label and label, and also meets the constraints of the indirect label. The present study also develops projection matrix normalization and prediction covariance analysis to ensure that the model can be fully trained. Finally, the effect of the physics-constrained indirect supervised learning is verified based on a well log generation problem.
