367 citations · 403 across the 11 of their papers we have counts for
11 papers
An Adaptive Port Technique for Synthesising Rotational Components in Component Modal Synthesis Approaches
Xiang Zhao, My Ha Dao
Component Modal Synthesis (CMS) is a reduced order modelling method widely used for large-scale complex systems. It can effectively approximate system-level models through componen…
Tailoring Generative Adversarial Networks for Smooth Airfoil Design
Joyjit Chattoraj, Jian Cheng Wong, Zhang Zexuan +8
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…
A Surrogate-Assisted Extended Generative Adversarial Network for Parameter Optimization in Free-Form Metasurface Design
Manna Dai, Yang Jiang, Feng Yang +6
Metasurfaces have widespread applications in fifth-generation (5G) microwave communication. Among the metasurface family, free-form metasurfaces excel in achieving intricate spectr…
Evolutionary Optimization of Physics-Informed Neural Networks: Advancing Generalizability by the Baldwin Effect
Jian Cheng Wong, Chin Chun Ooi, Abhishek Gupta +4
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 law…
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…
Graph Neural Network Based Surrogate Model of Physics Simulations for Geometry Design
Jian Cheng Wong, Chin Chun Ooi, Joyjit Chattoraj +6
Computational Intelligence (CI) techniques have shown great potential as a surrogate model of expensive physics simulation, with demonstrated ability to make fast predictions, albe…
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…