We propose conditioning field initialization for neural network based
topology optimization. In this work, we focus on (1) improving upon existing
neural network based topology optimization, (2) demonstrating that by using a
prior initial field on the unoptimized domain, the efficiency of neural network
based topology optimization can be further improved. Our approach consists of a
topology neural network that is trained on a case by case basis to represent
the geometry for a single topology optimization problem. It takes in domain
coordinates as input to represent the density at each coordinate where the
topology is represented by a continuous density field. The displacement is
solved through a finite element solver. We employ the strain energy field
calculated on the initial design domain as an additional conditioning field
input to the neural network throughout the optimization. The addition of the
strain energy field input improves the convergence speed compared to standalone
neural network based topology optimization.

本研究提出了基于神经网络的拓扑优化领域初始化的方法，并证明通过在未优化的区域上使用先前的初始场，可以进一步提高神经网络拓扑优化的效率。