367 citations · 487 across the 9 of their papers we have counts for
9 papers
LSA-PINN: Linear Boundary Connectivity Loss for Solving PDEs on Complex Geometry
Jian Cheng Wong, Pao-Hsiung Chiu, Chinchun Ooi +2
We present a novel loss formulation for efficient learning of complex dynamics from governing physics, typically described by partial differential equations (PDEs), using physics-i…
Design of Turing Systems with Physics-Informed Neural Networks
Jordon Kho, Winston Koh, Jian Cheng Wong +2
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 differ…
Robustness of Physics-Informed Neural Networks to Noise in Sensor Data
Jian Cheng Wong, Pao-Hsiung Chiu, Chin Chun Ooi +1
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-w…
FastFlow: AI for Fast Urban Wind Velocity Prediction
Shi Jer Low, Venugopalan, S. G. Raghavan +4
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 d…
CAN-PINN: A Fast Physics-Informed Neural Network Based on Coupled-Automatic-Numerical Differentiation Method
Pao-Hsiung Chiu, Jian Cheng Wong, Chinchun Ooi +2
In this study, novel physics-informed neural network (PINN) methods for coupling neighboring support points and their derivative terms which are obtained by automatic differentiati…
Improved Surrogate Modeling of Fluid Dynamics with Physics-Informed Neural Networks
Jian Cheng Wong, Chinchun Ooi, Pao-Hsiung Chiu +1
Physics-Informed Neural Networks (PINNs) have recently shown great promise as a way of incorporating physics-based domain knowledge, including fundamental governing equations, into…
Can Transfer Neuroevolution Tractably Solve Your Differential Equations?
Jian Cheng Wong, Abhishek Gupta, Yew-Soon Ong
This paper introduces neuroevolution for solving differential equations. The solution is obtained through optimizing a deep neural network whose loss function is defined by the res…
Will the US Economy Recover in 2010? A Minimal Spanning Tree Study
Yiting Zhang, Gladys Hui Ting Lee, Jian Cheng Wong +3
We calculated the cross correlations between the half-hourly times series of the ten Dow Jones US economic sectors over the period February 2000 to August 2008, the two-year interv…
Causal Links Between US Economic Sectors
Gladys Hui Ting Lee, Yiting Zhang, Jian Cheng Wong +2
In this paper, we perform a comparative segmentation and clustering analysis of the time series for the ten Dow Jones US economic sector indices between 14 February 2000 and 31 Aug…