CoPR-DTO: Collaboratively driven and periodically recalibrated dynamic topology optimization

Topology optimization seeks optimal material distribution in a design domain to meet constraints and maximize performance. While static load optimization is well-developed, real-world structures often face dynamic loads, making dynamic characteristics critical. Traditional dynamic topology optimization methods are computationally expensive due to iterative dynamic analysis, limiting their applicability to complex problems. This paper proposes a collaboratively driven dynamic topology optimization framework with periodic recalibration. It integrates two neural networks: a topology generation network mapping coordinates to element densities, and a U-Netbased dynamic response prediction network for fast displacement field prediction. A periodic recalibration strategy maintains model accuracy: initial high-fidelity data trains the prediction network, which then drives rapid optimization iterations, with periodic reversion to finite element solvers for data update and model recalibration. Dual loss functions and gradient separation ensure independent network optimization. This framework balances computational efficiency and physical consistency, offering an effective solution for complex dynamic topology optimization.