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 • 4 Feb 2021 • Hongwei Guo, Xiaoying Zhuang, Timon Rabczuk
In this paper, a deep collocation method (DCM) for thin plate bending problems is proposed.
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.
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.