367 citations · 487 across the 9 of their papers we have counts for
3 papers · 1 filter
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…