Zobrazeno 1 - 10
of 75
pro vyhledávání: '"Ooi, Chin Chun"'
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:
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
Reaction-diffusion (Turing) systems are fundamental to the formation of spatial patterns in nature and engineering. These systems are governed by a set of non-linear partial differential equations containing parameters that determine the rate of cons
Externí odkaz:
http://arxiv.org/abs/2211.13464
Autor:
Ho, Kong Yuan, Lim, Chin Seng, Kattar, Matthena A., Boppana, Bharathi, Yu, Liya, Ooi, Chin Chun
Traffic emissions are known to contribute significantly to air pollution around the world, especially in heavily urbanized cities such as Singapore. It has been previously shown that the particulate pollution along major roadways exhibit strong corre
Externí odkaz:
http://arxiv.org/abs/2211.13455
Physics-Informed Neural Networks (PINNs) have been shown to be an effective way of incorporating physics-based domain knowledge into neural network models for many important real-world systems. They have been particularly effective as a means of infe
Externí odkaz:
http://arxiv.org/abs/2211.12042
Autor:
Low, Shi Jer, Venugopalan, Raghavan, S. G., Gopalan, Harish, Wong, Jian Cheng, Yeoh, Justin, Ooi, Chin Chun
Data-driven approaches, including deep learning, have shown great promise as surrogate models across many domains. These extend to various areas in sustainability. An interesting direction for which data-driven methods have not been applied much yet
Externí odkaz:
http://arxiv.org/abs/2211.12035
Numerical weather prediction (NWP) and machine learning (ML) methods are popular for solar forecasting. However, NWP models have multiple possible physical parameterizations, which requires site-specific NWP optimization. This is further complicated
Externí odkaz:
http://arxiv.org/abs/2112.04963
Autor:
Klaseboer, Evert, Leong, Fong Yew, Ooi, Chin Chun, Ge, Zhengwei, Kang, Chang Wei, Cai, Wentong, Wang, Victor Peng Cheng, Ang, Elisa Yun Mei, Goh, Max Han Sheng, Beshay, Peter Farouk Ramzy, Fong, Siew Wai, Tay, Matthew Zirui, Li, Hongying
Publikováno v:
In Chemical Engineering Science 5 October 2024 298
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
Microfluidics have shown great promise in multiple applications, especially in biomedical diagnostics and separations. While the flow properties of these microfluidic devices can be solved by numerical methods such as computational fluid dynamics (CF
Externí odkaz:
http://arxiv.org/abs/2105.05173