no code implementations • 18 Mar 2024 • Yizheng Wang, Xiang Li, Ziming Yan, Yuqing Du, Jinshuai Bai, Bokai Liu, Timon Rabczuk, Yinghua Liu
Homogenization is an essential tool for studying multiscale physical phenomena.
1 code implementation • 3 Feb 2023 • Yizheng Wang, Jia Sun, Timon Rabczuk, Yinghua Liu
The results demonstrate that DCEM outperforms DEM in terms of stress accuracy and efficiency and has an advantage in dealing with complex displacement boundary conditions, which is supported by theoretical analyses and numerical simulations.
no code implementations • 21 Dec 2021 • Ayan Chakraborty, Thomas Wick, Xiaoying Zhuang, Timon Rabczuk
An efficient and easy to implement algorithm is developed to obtain a posteriori error estimate for multiple goal functionals by employing the dual-weighted residual approach, which is followed by the computation of both primal and adjoint solutions using the neural network.
no code implementations • 19 Nov 2021 • Zhizheng Jiang, Fei Gao, Renshu Gu, Jinlan Xu, Gang Xu, Timon Rabczuk
In this paper, a novel deep learning framework is proposed for temporal super-resolution simulation of blood vessel flows, in which a high-temporal-resolution time-varying blood vessel flow simulation is generated from a low-temporal-resolution flow simulation result.
no code implementations • 4 Feb 2021 • Hongwei Guo, Xiaoying Zhuang, Timon Rabczuk
In this paper, a deep collocation method (DCM) for thin plate bending problems is proposed.
1 code implementation • 15 Dec 2020 • Abhinav Gupta, Rajib Chowdhury, Anupam Chakrabarti, Timon Rabczuk
This paper presents a 55-line code written in python for 2D and 3D topology optimization (TO) based on the open-source finite element computing software (FEniCS), equipped with various finite element tools and solvers.
Mathematical Software Computational Engineering, Finance, and Science Optimization and Control
no code implementations • 9 Oct 2020 • Xiaoying Zhuang, Hongwei Guo, Naif Alajlan, Timon Rabczuk
In this paper, we present a deep autoencoder based energy method (DAEM) for the bending, vibration and buckling analysis of Kirchhoff plates.
no code implementations • 3 Oct 2020 • Hongwei Guo, Xiaoying Zhuang, Timon Rabczuk
In this work, a modified neural architecture search method (NAS) based physics-informed deep learning model is presented for stochastic analysis in heterogeneous porous material.
no code implementations • 3 Oct 2020 • Hongwei Guo, Xiaoying Zhuang, Pengwan Chen, Naif Alajlan, Timon Rabczuk
This approach utilizes a physics informed neural network with material transfer learning reducing the solution of the nonhomogeneous partial differential equations to an optimization problem.
no code implementations • 11 Sep 2020 • Aydin Shishegaran, Hessam Varaee, Timon Rabczuk, Gholamreza Shishegaran
HCVCM improves the accuracy of ANFIS by 5% in the coefficient of determination, 10% in RMSE, 3% in NMSE, 20% in MAPE, and 7% in the maximum negative error.
no code implementations • 20 Dec 2019 • Zohreh Sheikh Khozani, Khabat Khosravi, Mohammadamin Torabi, Amir Mosavi, Bahram Rezaei, Timon Rabczuk
Finally, the most powerful data mining method which studied in this research (RF) compared with two well-known analytical models of Shiono and Knight Method (SKM) and Shannon method to acquire the proposed model functioning in predicting the shear stress distribution.
1 code implementation • 27 Aug 2019 • Esteban Samaniego, Cosmin Anitescu, Somdatta Goswami, Vien Minh Nguyen-Thanh, Hongwei Guo, Khader Hamdia, Timon Rabczuk, Xiaoying Zhuang
In this contribution, we explore Deep Neural Networks (DNNs) as an option for approximation.
no code implementations • 4 Jul 2019 • Somdatta Goswami, Cosmin Anitescu, Souvik Chakraborty, Timon Rabczuk
While most of the PINN algorithms available in the literature minimize the residual of the governing partial differential equation, the proposed approach takes a different path by minimizing the variational energy of the system.
no code implementations • 26 May 2019 • Shahaboddin Shamshirband, Amir Mosavi, Timon Rabczuk
To improve the efficiency of the proposed model, individual equations are derived for laboratory and field data.