TL;DR提出了利用物理知识来升级神经网络模型以解决优化问题的方法，通过使用修正线性单元和分段线性逼近的双曲正切激活函数，针对三个不同的案例进行实验，结果表明这种升级模型比传统模型更接近于全局最优解，且更有效地优化了 CPU 时间。
Abstract
Constructing first-principles models is usually a challenging and
time-consuming task due to the complexity of the real-life processes. On the
other hand, data-driven modeling, and in particular neural network models often
suffer from issues such as overfitting and lack of useful and highquality data.
At the same time, embedding trained machine learning mode