3 citations · 5 across the 4 of their papers we have counts for
4 papers
Dynamically configured physics-informed neural network in topology optimization applications
Jichao Yin, Ziming Wen, Shuhao Li +2
Integration of machine learning (ML) into the topology optimization (TO) framework is attracting increasing attention, but data acquisition in data-driven models is prohibitive. Co…
Resolution-independent generative models based on operator learning for physics-constrained Bayesian inverse problems
Xinchao Jiang, Xin Wang, Ziming Wen +1
The Bayesian inference approach is widely used to tackle inverse problems due to its versatile and natural ability to handle ill-posedness. However, it often faces challenges when…
An E-PINN assisted practical uncertainty quantification for inverse problems
Xinchao Jiang, Xin Wanga, Ziming Wena +2
How to solve inverse problems is the challenge of many engineering and industrial applications. Recently, physics-informed neural networks (PINNs) have emerged as a powerful approa…
A novel generative reverse net assisted evolution algorithm for expensive-computational optimizations
Yu Li, Hu Wang, Ziming Wen +1
Simulation-based optimization is a useful method for practical design problems. However, it is difficult for complicated problems due to expensive-computational costs. A popular wa…