论文标题
次级BJERKNES力量的机器学习模型在两个受害的气泡之间
Machine learning models for the secondary Bjerknes force between two insonated bubbles
论文作者
论文摘要
次级BJERKNES力在气泡簇的演化中起着重要作用。但是，由于力对多个参数的复杂依赖性，将这种力的效果包括在气泡簇的模拟中是高度的。在本文中，机器学习用于开发一个数据驱动的模型，用于在两个被互换的气泡之间的二级BJERKNES力，这是气泡平衡半径的函数，气泡之间的距离，幅度和压力频率。该力在几个数量级上有所不同，这对通常的机器学习模型构成了严重的挑战。为了克服这一难度，将力的幅度和力符号分开和建模。通过用于大小的对数的馈电网络模型获得了非线性回归，而符号是由支持矢量机模型建模的。引入了与机器模型的培训和验证有关的原理。根据Keller-Miksis方程计算的值检查模型的预测。结果表明，模型非常有效，同时提供了对力的准确估计。这些模型使其在计算上是可行的，以使气泡簇的未来模拟包括次级BJERKNES力的效果。
The secondary Bjerknes force plays a significant role in the evolution of bubble clusters. However, due to the complex dependence of the force on multiple parameters, it is highly non-trivial to include the effects of this force in the simulations of bubble clusters. In this paper, machine learning is used to develop a data-driven model for the secondary Bjerknes force between two insonated bubbles as a function of the equilibrium radii of the bubbles, the distance between the bubbles, the amplitude and the frequency of the pressure. The force varies over several orders of magnitude, which poses a serious challenge for the usual machine learning models. To overcome this difficulty, the magnitudes and the signs of the force are separated and modelled separately. A nonlinear regression is obtained with a feed-forward network model for the logarithm of the magnitude, whereas the sign is modelled by a support-vector machine model. The principle, the practical aspects related to the training and validation of the machine models are introduced. The predictions from the models are checked against the values computed from the Keller-Miksis equations. The results show that the models are extremely efficient while providing accurate estimate of the force. The models make it computationally feasible for the future simulations of the bubble clusters to include the effects of the secondary Bjerknes force.
