94 citations · 122 across the 4 of their papers we have counts for
5 papers
Hybrid epidemic simulation framework coupling equation-based and individual-based models
Jaeyoung Kwak, Michael H. Lees, Chin Chun Ooi +1
Mass gathering events like concerts, sports matches, and festivals bring many people into close contact within a short period, creating localized bursts of infection that can shape…
FFV-PINN: A Fast Physics-Informed Neural Network with Simplified Finite Volume Discretization and Residual Correction
Chang Wei, Yuchen Fan, Jian Cheng Wong +3
Physics-informed neural networks (PINNs) have emerged as a major research focus. However, today's PINNs encounter several limitations. Firstly, during the construction of the loss…
CompARE: A Computational framework for Airborne Respiratory disease Evaluation integrating flow physics and human behavior
Fong Yew Leong, Jaeyoung Kwak, Zhengwei Ge +7
The risk of indoor airborne transmission among co-located individuals is generally non-uniform, which remains a critical challenge for public health modelling. Thus, we present Com…
Differentiable Physics-Neural Models enable Learning of Non-Markovian Closures for Accelerated Coarse-Grained Physics Simulations
Tingkai Xue, Chin Chun Ooi, Zhengwei Ge +3
Numerical simulations provide key insights into many physical, real-world problems. However, while these simulations are solved on a full 3D domain, most analysis only require a re…
Learning in Sinusoidal Spaces with Physics-Informed Neural Networks
Jian Cheng Wong, Chinchun Ooi, Abhishek Gupta +1
A physics-informed neural network (PINN) uses physics-augmented loss functions, e.g., incorporating the residual term from governing partial differential equations (PDEs), to ensur…