3 citations · 8 across the 6 of their papers we have counts for
5 papers · 1 filter
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…
A novel reduced basis method for adjoint sensitivity analysis of dynamic topology optimization
Shuhao Li, Hu Wang, Jichao Yin +2
In gradient-based time domain topology optimization, design sensitivity analysis (DSA) of the dynamic response is essential, and requires high computational cost to directly differ…
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 Physics-Data-Driven Bayesian Method for Heat Conduction Problems
Xinchao Jiang, Hu Wang, Yu li
In this study, a novel physics-data-driven Bayesian method named Heat Conduction Equation assisted Bayesian Neural Network (HCE-BNN) is proposed. The HCE-BNN is constructed based o…
Machine Learning based parameter tuning strategy for MMC based topology optimization
Xinchao Jiang, Hu Wang, Yu Li +1
Moving Morphable Component (MMC) based topology optimization approach is an explicit algorithm since the boundary of the entity explicitly described by its functions. Compared with…