论文标题
使用神经普通微分方程对失真电路的虚拟模拟建模
Virtual Analog Modeling of Distortion Circuits Using Neural Ordinary Differential Equations
论文作者
论文摘要
深度学习的最新研究表明，神经网络可以学习有关动态系统的微分方程。在本文中，我们将此概念调整到虚拟模拟（VA）建模中，以学习管理一阶和二阶二极管剪辑的普通微分方程（ODE）。提出的模型实现了与最新的复发神经网络（RNN）相当的性能，尽管使用了较少的参数。我们表明，这种方法不需要过度采样，并且允许在培训完成后提高采样率，从而提高了准确性。使用复杂的数值求解器可以以较慢的处理成本提高准确性。以这种方式学到的ODE不需要封闭的表格，但仍然可以在物理上解释。
Recent research in deep learning has shown that neural networks can learn differential equations governing dynamical systems. In this paper, we adapt this concept to Virtual Analog (VA) modeling to learn the ordinary differential equations (ODEs) governing the first-order and the second-order diode clipper. The proposed models achieve performance comparable to state-of-the-art recurrent neural networks (RNNs) albeit using fewer parameters. We show that this approach does not require oversampling and allows to increase the sampling rate after the training has completed, which results in increased accuracy. Using a sophisticated numerical solver allows to increase the accuracy at the cost of slower processing. ODEs learned this way do not require closed forms but are still physically interpretable.
