Zobrazeno 1 - 10
of 33
pro vyhledávání: '"Jiang, Xinchao"'
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 dealing with situations involving continuous fields or large-resolutio
Externí odkaz:
http://arxiv.org/abs/2308.13295
In gradient-based time domain topology optimization, design sensitivity analysis (DSA) of the dynamic response is essential, and requires high computational cost to directly differentiate, especially for high-order dynamic system. To address this iss
Externí odkaz:
http://arxiv.org/abs/2308.09864
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 approach to solve inverse problems efficiently. However, it is difficult for
Externí odkaz:
http://arxiv.org/abs/2209.10195
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 on the Bayesian neural network, it is a physics-informed machine learni
Externí odkaz:
http://arxiv.org/abs/2109.00996
Publikováno v:
In Engineering Analysis with Boundary Elements May 2024 162:403-419
Publikováno v:
In Computer Methods in Applied Mechanics and Engineering 15 February 2024 420
Publikováno v:
In International Communications in Heat and Mass Transfer October 2023 147
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 other pixel or node point-based algorithms, it is optimized through t
Externí odkaz:
http://arxiv.org/abs/1910.07227
Publikováno v:
In Advanced Engineering Informatics August 2023 57
In this study, an image-assisted Approximate Bayesian Computation (ABC) parameter inverse method is proposed to identify the design parameters. In the proposed method, the images are mapped to a low-dimensional latent space by Variational Auto-Encode
Externí odkaz:
http://arxiv.org/abs/1907.03560