Multiphysics Modeling of Voice Coil Actuators with Recurrent Neural Network

In order to accurately model the behaviors of a voice coil actuator (VCA), the three-dimensional (3-D) method is preferred over a lumped model. However, generating a 3-D model of a VCA system can be very computationally expensive. The computation efficiency is often limited by the spatial discretization, the multiphysics nature, and the nonlinearities of the VCA system. In order to enhance the computation efficiency, we propose incorporating the recurrent neural network (RNN) into the multiphysics simulation. In the proposed approach, the multiphysics problem is first solved with the finite element method (FEM) at full 3-D accuracy within a portion of the required time steps. A RNN is trained and validated with the obtained transient solutions. Once the training completes, the RNN can make predictions and generate results on the remaining portion of the required time steps. With the proposed approach, it avoids solving the multiphysics problem at all time steps and a significant reduction of computation time can thus be achieved. The training cost of the RNN model can be amortized when a longer duration of the transient solutions is required. A numerical example is used to demonstrate the improvement on the computation efficiency. Topologies of the neural network and the tunable parameters are investigated with the numerical example.

• General Science

• Machine Intelligence