论文标题
使用物理信息的神经网络转移学习，以有效地模拟分支流
Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows
论文作者
论文摘要
物理知识的神经网络（PINN）提供了一种有前途的方法来解决微分方程，更普遍地将深度学习应用于物理科学的问题。我们为PINN采用了最近开发的转移学习方法，并引入了多头模型，以有效地获得具有随机电势的普通微分方程的非线性系统的准确解决方案。特别是，我们应用了该方法来模拟随机波动力学中的通用现象随机分支流。最后，我们比较了Feed Forward和基于GAN的PINN在两个物理相关的转移学习任务上获得的结果，并表明我们的方法与从头开始训练的标准PINN相比提供了显着的计算加速。
Physics-Informed Neural Networks (PINNs) offer a promising approach to solving differential equations and, more generally, to applying deep learning to problems in the physical sciences. We adopt a recently developed transfer learning approach for PINNs and introduce a multi-head model to efficiently obtain accurate solutions to nonlinear systems of ordinary differential equations with random potentials. In particular, we apply the method to simulate stochastic branched flows, a universal phenomenon in random wave dynamics. Finally, we compare the results achieved by feed forward and GAN-based PINNs on two physically relevant transfer learning tasks and show that our methods provide significant computational speedups in comparison to standard PINNs trained from scratch.
