1 code implementation • 6 Dec 2023 • Jian Cheng Wong, Chin Chun Ooi, Abhishek Gupta, Pao-Hsiung Chiu, Joshua Shao Zheng Low, My Ha Dao, Yew-Soon Ong
Physics-informed neural networks (PINNs) are at the forefront of scientific machine learning, making possible the creation of machine intelligence that is cognizant of physical laws and able to accurately simulate them.
no code implementations • 18 Oct 2023 • Manna Dai, Yang Jiang, Feng Yang, Joyjit Chattoraj, Yingzhi Xia, Xinxing Xu, Weijiang Zhao, My Ha Dao, Yong liu
Metasurfaces have widespread applications in fifth-generation (5G) microwave communication.
no code implementations • 3 Feb 2023 • Jian Cheng Wong, Pao-Hsiung Chiu, Chinchun Ooi, My Ha Dao, Yew-Soon Ong
On the other hand, if the samples are too sparse, existing PINNs tend to overfit the near boundary region, leading to incorrect solution.
no code implementations • 1 Feb 2023 • Jian Cheng Wong, Chin Chun Ooi, Joyjit Chattoraj, Lucas Lestandi, Guoying Dong, Umesh Kizhakkinan, David William Rosen, Mark Hyunpong Jhon, My Ha Dao
Computational Intelligence (CI) techniques have shown great potential as a surrogate model of expensive physics simulation, with demonstrated ability to make fast predictions, albeit at the expense of accuracy in some cases.
no code implementations • 29 Oct 2021 • Pao-Hsiung Chiu, Jian Cheng Wong, Chinchun Ooi, My Ha Dao, Yew-Soon Ong
In this study, novel physics-informed neural network (PINN) methods for coupling neighboring support points and their derivative terms which are obtained by automatic differentiation (AD), are proposed to allow efficient training with improved accuracy.
no code implementations • 5 May 2021 • Jian Cheng Wong, Chinchun Ooi, Pao-Hsiung Chiu, My Ha Dao
In addition, we propose a novel transfer optimization scheme for use in such surrogate modeling scenarios and demonstrate an approximately 3x improvement in speed to convergence and an order of magnitude improvement in predictive performance over conventional Xavier initialization for training of new scenarios.