367 citations · 403 across the 11 of their papers we have counts for
5 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…
Trait-based numerical model for mixotrophic phytoplankton and application in Singapore water
My Ha Dao
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…
Projection-Based Reduced Order Model for Simulations of Nonlinear Flows with Multiple Moving Objects
My Ha Dao
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-Based Reduced Order Model and Machine Learning Closure for Transient Simulations of High-Re Flows
My Ha Dao, Hoang Huy Nguyen
The paper presents a Projection-Based Reduced-Order Model for simulations of high Reynolds turbulent flows. The PBROM are enhanced by incorporating various models of turbulent visc…
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…