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Dynamically configured physics-informed neural network in topology optimization applications
Computer Methods in Applied Mechanics and Engineering ( IF 7.2 ) Pub Date : 2024-04-26 , DOI: 10.1016/j.cma.2024.117004
Jichao Yin , Ziming Wen , Shuhao Li , Yaya Zhang , Hu Wang

The integration of physics-informed neural network (PINN) and topology optimization (TO) is an attractive issue because PINN can avoid the prohibitive data acquisition of solving forward problems compared to traditional machine learning. To enhance the efficiency of this integrated optimization framework, a dynamically configured PINN-based topology optimization (DCPINN-TO) method is proposed in conjunction with an active sampling strategy. The DCPINN comprises two sub-networks with distinct training costs, capable of dynamically adjusting the trainable parameters based on the optimization state of TO. Moreover, the active sampling strategy selectively samples collocations based on the pseudo-densities, which can significantly reduce training costs by decreasing the number of input collocations. Additionally, the Gaussian integral is used to calculate the strain energy of elements to decouple the mapping of the material at the collocations. The proposed method is extended to various scenarios, including those with high resolution, multiple loads, and displacement constraints. Its efficiency and generalization are validated by several illustrative examples. Furthermore, the accuracy of DCPINN versus finite element analysis-based TO (FEA-TO) was also investigated.

中文翻译:


拓扑优化应用中动态配置的物理信息神经网络



物理信息神经网络(PINN)和拓扑优化(TO)的集成是一个有吸引力的问题,因为与传统机器学习相比,PINN 可以避免解决正向问题时令人望而却步的数据采集。为了提高该集成优化框架的效率,结合主动采样策略,提出了一种动态配置的基于 PINN 的拓扑优化(DCPINN-TO)方法。 DCPINN由两个具有不同训练成本的子网络组成,能够根据TO的优化状态动态调整可训练参数。此外,主动采样策略根据伪密度选择性地对搭配进行采样,这可以通过减少输入搭配的数量来显着降低训练成本。此外,高斯积分用于计算单元的应变能,以解耦材料在搭配处的映射。该方法可扩展到各种场景,包括高分辨率、多载荷和位移约束的场景。它的效率和泛化性通过几个说明性例子得到了验证。此外,还研究了 DCPINN 与基于有限元分析的 TO (FEA-TO) 的准确性。
更新日期:2024-04-26
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