Topology Optimization using Neural Networks with Conditioning Field
  Initialization for Improved Efficiency
Hongrui Chen Aditya Joglekar Levent Burak Kara
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.