Zobrazeno 1 - 10
of 195
pro vyhledávání: '"Dao My"'
Autor:
Chattoraj, Joyjit, Wong, Jian Cheng, Zexuan, Zhang, Dai, Manna, Yingzhi, Xia, Jichao, Li, Xinxing, Xu, Chun, Ooi Chin, Feng, Yang, Ha, Dao My, Yong, Liu
In the realm of aerospace design, achieving smooth curves is paramount, particularly when crafting objects such as airfoils. Generative Adversarial Network (GAN), a widely employed generative AI technique, has proven instrumental in synthesizing airf
Externí odkaz:
http://arxiv.org/abs/2404.11816
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. However, today's PINNs are oft
Externí odkaz:
http://arxiv.org/abs/2312.03243
Publikováno v:
Parkinson's Disease, Vol 2019 (2019)
Parkinson’s disease (PD) is a common neurodegenerative disorder and characterized by progressive locomotive defects and loss of dopaminergic neurons (DA neuron). Currently, there is no potent therapy to cure PD, and the medications merely support t
Externí odkaz:
https://doaj.org/article/9377dbf2f8c842a08d6cd11ab07d81dd
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
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