Zobrazeno 1 - 10
of 81
pro vyhledávání: '"Dao, My Ha"'
Autor:
Wong, Jian Cheng, Ooi, Chin Chun, Gupta, Abhishek, Chiu, Pao-Hsiung, Low, Joshua Shao Zheng, Dao, My Ha, Ong, Yew-Soon
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. In this paper, the potential o
Externí odkaz:
http://arxiv.org/abs/2312.03243
Autor:
Dai, Manna, Jiang, Yang, Yang, Feng, Chattoraj, Joyjit, Xia, Yingzhi, Xu, Xinxing, Zhao, Weijiang, Dao, My Ha, Liu, Yong
Metasurfaces have widespread applications in fifth-generation (5G) microwave communication. Among the metasurface family, free-form metasurfaces excel in achieving intricate spectral responses compared to regular-shape counterparts. However, conventi
Externí odkaz:
http://arxiv.org/abs/2401.02961
Publikováno v:
2023 International Joint Conference on Neural Networks (IJCNN)
We present a novel loss formulation for efficient learning of complex dynamics from governing physics, typically described by partial differential equations (PDEs), using physics-informed neural networks (PINNs). In our experiments, existing versions
Externí odkaz:
http://arxiv.org/abs/2302.01518
Autor:
Wong, Jian Cheng, Ooi, Chin Chun, Chattoraj, Joyjit, Lestandi, Lucas, Dong, Guoying, Kizhakkinan, Umesh, Rosen, David William, Jhon, Mark Hyunpong, Dao, My Ha
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. For many scientific and e
Externí odkaz:
http://arxiv.org/abs/2302.00557
Autor:
Dai, Manna, Jiang, Yang, Yang, Feng, Chattoraj, Joyjit, Xia, Yingzhi, Xu, Xinxing, Zhao, Weijiang, Dao, My Ha, Liu, Yong
Publikováno v:
In Neural Networks December 2024 180
Publikováno v:
Computer Methods in Applied Mechanics and Engineering, Volume 395, 15 May 2022, 114909
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 accura
Externí odkaz:
http://arxiv.org/abs/2110.15832
Autor:
Dao, My Ha, Le, Quang Tuyen, Zhao, Xiang, Ooi, Chin Chun, Duong, Luu Trung Pham, Raghavan, Nagarajan
Publikováno v:
In Renewable Energy June 2024 227
Publikováno v:
In Renewable Energy April 2024 224
Autor:
Dao, My Ha
A numerical model for mixotrophic phytoplankton is described in this paper. In contrast with traditional approach where nutrient uptake rates are constrained by a predefined growth rate, this model uses empirical traits to compute nutrient uptake rat
Externí odkaz:
http://arxiv.org/abs/2106.08227
Projection-Based Reduced Order Model for Simulations of Nonlinear Flows with Multiple Moving Objects
Autor:
Dao, My Ha
This paper presents a reduced order approach for transient modeling of multiple moving objects in nonlinear crossflows. The Proper Orthogonal Decomposition method and the Galerkin projection are used to construct a reduced version of the nonlinear Na
Externí odkaz:
http://arxiv.org/abs/2106.02338