3 citations · 3 across the 3 of their papers we have counts for
3 papers
math.NA2023
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…
math.NA2022★ 3 cited
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…
eess.SP2019
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…